Storage system often applies erasure codes to protect against disk failure and ensure system reliability and
availability. Liberation code that is a type of coding scheme has been widely used in many storage systems
because its encoding and modifying operations are efficient. However, it cannot effectively achieve fast recovery
from single disk failure in storage systems, and has great influence on recovery performance as well as response
time of client requests. To solve this problem, in this paper, we present HRSF, a Hybrid Recovery method for
solving Single disk Failure. We present the optimal algorithm to accelerate failure recovery process. Theoretical
analysis proves that our scheme consumes approximately 25% less amount of data read than the conventional
method. In the evaluation, we perform extensive experiments by setting different number of disks and chunk
sizes. The results show that HRSF outperforms conventional method in terms of the amount of data read and
failure recovery time.

In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple power
quality disturbances. To solve the problem of parameter selection, an improved particle swarm optimization
algorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertia
weight expression, the particle swarm optimization algorithm can effectively conduct a global search at the
outset and effectively search locally later in a study, which improves the overall classification accuracy. The
experimental results show that the improved particle swarm optimization method is more accurate than a grid
search algorithm optimization and other improved particle swarm optimizations with regard to its classification
of multiple power quality disturbances. Furthermore, the number of support vectors is reduced.

With the recent advances of memory technologies, high-performance non-volatile memories such as nonvolatile
dual in-line memory module (NVDIMM) have begun to be used as an addition or an alternative to
server-side storages. When these memory bus-connected storages (MBSs) are installed over non-uniform
memory access (NUMA) servers, the distance between NUMA nodes and MBSs is one of the crucial factors
that influence file processing performance, because the access latency of a NUMA system varies depending on
its distance from the NUMA nodes. This paper presents the design and implementation of a high-performance
logical volume manager for MBSs, called MBS-LVM, when multiple MBSs are scattered over a NUMA server.
The MBS-LVM consolidates the address space of each MBS into a single global address space and dynamically
utilizes storage spaces such that each thread can access an MBS with the lowest latency possible. We
implemented the MBS-LVM in the Linux kernel and evaluated its performance by porting it over the tmpfs, a
memory-based file system widely used in Linux. The results of the benchmarking show that the write
performance of the tmpfs using MBS-LVM has been improved by up to twenty times against the original tmpfs
over a NUMA server with four nodes.

Transmitting visual information over a broadcasting network is not only prone to a copyright violation but also
is a forgery. Authenticating such information and protecting its authorship rights call for more advanced data
encoding. To this end, electronic watermarking is often adopted to embed inscriptive signature in imaging data.
Most existing watermarking methods while focusing on robustness against degradation remain lacking of
measurement against security loophole in which the encrypting scheme once discovered may be recreated by
an unauthorized party. This could reveal the underlying signature which may potentially be replaced or forged.
This paper therefore proposes a novel digital watermarking scheme in temporal-frequency domain. Unlike
other typical wavelet based watermarking, the proposed scheme employed the Lorenz chaotic map to specify
embedding positions. Effectively making this is not only a formidable method to decrypt but also a stronger
will against deterministic attacks. Simulation report herein highlights its strength to withstand spatial and
frequent adulterations, e.g., lossy compression, filtering, zooming and noise.

A production system is a management system that supports all activities to perform production operations at
the manufacturing site. From the point-of-view of a smart factory, smart manufacturing systems redesigned the
concept of onsite production systems to fit the entire system and its necessary functional composition. In this
study, we select the key functions needed to build a smart factory for a PCB line and propose a new six-step
model for the deployment of a smart manufacturing system by integrating essential functions. The smart
manufacturing system newly classified the production and operation tasks of PCB manufacturing and selected
necessary functions through requirement analysis and benchmarking of advanced companies. The selected
production operation tasks are mapped to the functions of the system and configured into seven modules, and
the optimal deployment model is presented to allow flexible responses to the characteristics of the tasks. These
methodologies are first presented in this study, and the proposed model was applied to the PCB line to confirm
that they had significant changes in the work method, qualitative effects, and quantitative effects. Typically, lead
time and WIP have reduced by about 50%.

Currently, the crew working on a ship is required to carry a seafarer's book in most countries around the world,
including the Republic of Korea (ROK). Yet, many fishermen working in the international waters of the ROK
do not abide by this rule as the procedure of obtaining it is rather inconvenient or they do not understand the
necessity or the benefits of having it. Also, as the regulation of carrying the certificate has been strengthened, it
is important for them to avoid making a criminal record unintentionally. This study discusses the digitalization
of the seafarer’s book based on several security measures in addition to BLE Beacon-based positioning
technology, which can be useful for the e-Navigation. Normally, seamen’s certificates are recorded by the
captain, medical institution, or issuing authority and then kept in an onboard safe or a certificate cabinet. The
material of the certificates is a cloth that can withstand salinity as the certificate could be contaminated by mold.
In the past, the captains and their crews were uncooperative when the ROK’s maritime police tried to inspect
several ships simultaneously because of the time and cost involved. Thus, a system with which the maritime
police will be able to conveniently manage the crews is proposed.

An individual’s health data is very sensitive and private. Such data are usually stored on a private or community owned cloud, where access is not restricted to the owners of that cloud. Anyone within the cloud can access this data. This data may not be read only and multiple parties can make to it. Thus, any unauthorized modification of health-related data will lead to incorrect diagnosis and mistreatment. However, we cannot restrict semipublic access to this data. Existing security mechanisms in e-health systems are competent in dealing with the issues associated with these systems but only up to a certain extent. The indigenous technologies need to be complemented with current and future technologies. We have put forward a method to complement such technologies by incorporating the concept of blockchain to ensure the integrity of data as well as its provenance.

This paper presents an optimal implementation of a Daubechies-based pipelined discrete wavelet packet
transform (DWPT) processor using finite impulse response (FIR) filter banks. The feed-forward pipelined (FFP)
architecture is exploited for implementation of the DWPT on the field-programmable gate array (FPGA). The
proposed DWPT is based on an efficient transpose form structure, thereby reducing its computational complexity
by half of the system. Moreover, the efficiency of the design is further improved by using a canonical-signed
digit-based binary expression (CSDBE) and advanced functional sharing (AFS) methods. In this work, the AFS
technique is proposed to optimize the convolution of FIR filter banks for DWPT decomposition, which reduces
the hardware resource utilization by not requiring any embedded digital signal processing (DSP) blocks. The
proposed AFS and CSDBE-based DWPT system is embedded on the Virtex-7 FPGA board for testing. The
proposed design is implemented as an intellectual property (IP) logic core that can easily be integrated into DSP
systems for sub-band analysis. The achieved results conclude that the proposed method is very efficient in
improving hardware resource utilization while maintaining accuracy of the result of DWPT.

Three-dimensional (3D) human pose reconstruction from single-view image is a difficult and challenging topic.
Existing approaches mostly process frame-by-frame independently while inter-frames are highly correlated in
a sequence. In contrast, we introduce a novel spatial-temporal 3D human pose reconstruction framework that
leverages both intra and inter-frame relationships in consecutive 2D pose sequences. Orthogonal matching
pursuit (OMP) algorithm, pre-trained pose-angle limits and temporal models have been implemented. Several
quantitative comparisons between our proposed framework and recent works have been studied on CMU
motion capture dataset and Vietnamese traditional dance sequences. Our framework outperforms others by
10% lower of Euclidean reconstruction error and more robust against Gaussian noise. Additionally, it is also
important to mention that our reconstructed 3D pose sequences are more natural and smoother than others.

Artificial bee colony algorithm is a strong global search algorithm which exhibits excellent exploration ability.
The conventional ABC algorithm adopts employed bees, onlooker bees and scouts to cooperate with each other.
However, its one dimension and greedy search strategy causes slow convergence speed. To enhance its
performance, in this paper, we abandon the greedy selection method and propose an artificial bee colony
algorithm with special division and intellective search (ABCIS). For the purpose of higher food source research
efficiency, different search strategies are adopted with different employed bees and onlooker bees. Experimental
results on a series of benchmarks algorithms demonstrate its effectiveness.

The blooming of social media has simulated interest in sentiment analysis. Sentiment analysis aims to
determine from a specific piece of content the overall attitude of its author in relation to a specific item, product,
brand, or service. In sentiment analysis, the focus is on the subjective sentences. Hence, in order to discover
and extract the subjective information from a given text, researchers have applied various methods in
computational linguistics, natural language processing, and text analysis. The aim of this paper is to provide an
in-depth up-to-date study of the sentiment analysis algorithms in order to familiarize with other works done in
the subject. The paper focuses on the main tasks and applications of sentiment analysis. State-of-the-art
algorithms, methodologies and techniques have been categorized and summarized to facilitate future research
in this field.

Single-user spectrum sensing is susceptible to multipath effects, shadow effects, hidden terminals and other
unfavorable factors, leading to misjudgment of perceived results. In order to increase the detection accuracy
and reduce spectrum sensing cost, we propose an adaptive cooperative sensing strategy based on an estimated
signal-to-noise ratio (SNR). Which can adaptive select different sensing strategy during the local sensing phase.
When the estimated SNR is higher than the selection threshold, adaptive double threshold energy detector (ED)
is implemented, otherwise cyclostationary feature detector is performed. Due to the fact that only a better
sensing strategy is implemented in a period, the detection accuracy is improved under the condition of low SNR
with low complexity. The local sensing node transmits the perceived results through the control channel to the
fusion center (FC), and uses voting rule to make the hard decision. Thus the transmission bandwidth is
effectively saved. Simulation results show that the proposed scheme can effectively improve the system
detection probability, shorten the average sensing time, and has better robustness without largely increasing
the costs of sensing system.

Dynamic thermal rating technology can effectively improve the thermal load capacity of transmission lines.
However, its availability is limited by the quantity and high cost of the hardware facilities. This paper proposes
a new dynamic thermal rating technology based on global/regional assimilation and prediction system
(GRAPES) and geographic information system (GIS). The paper will also explore the method of obtaining any
point meteorological data along the transmission line by using GRAPES and GIS, and provide the strategy of
extracting and decoding meteorological data. In this paper, the accuracy of numerical weather prediction was
verified from the perspective of time and space. Also, the 750-kV transmission line in Shaanxi Province is
considered as an example to analyze. The results of the study indicate that dynamic thermal rating based on
GRAPES and GIS can fully excavate the line power potential without additional cost on hardware, which saves
a lot of investment.

Shape description is an important and fundamental issue in content-based image retrieval (CBIR), and a
number of shape description methods have been reported in the literature. For shape description, both global
information and local contour variations play important roles. In this paper a new included-angular ternary
pattern (IATP) based shape descriptor is proposed for shape image retrieval. For each point on the shape
contour, IATP is derived from its neighbor points, and IATP has good properties for shape description. IATP
is intrinsically invariant to rotation, translation and scaling. To enhance the description capability, multiscale
IATP histogram is presented to describe both local and global information of shape. Then multiscale IATP
histogram is combined with included-angular histogram for efficient shape retrieval. In the matching stage,
cosine distance is used to measure shape features’ similarity. Image retrieval experiments are conducted on the
standard MPEG-7 shape database and Swedish leaf database. And the shape image retrieval performance of the
proposed method is compared with other shape descriptors using the standard evaluation method. The
experimental results of shape retrieval indicate that the proposed method reaches higher precision at the same
recall value compared with other description method.

Internet of Things (IoT) is the paradigm of network of Internet-connected things as objects that constantly
sense the physical world and share the data for further processing. At the core of IoT lies the early technology
of radio frequency identification (RFID), which provides accurate location tracking of real-world objects. With
its small size and convenience, RFID tags can be attached to everyday items such as books, clothes, furniture
and the like as well as to animals, plants, and even humans. This phenomenon is the beginning of new
applications and services for the industry and consumer market. IoT is regarded as a fourth industrial
revolution because of its massive coverage of services around the world from smart homes to artificial
intelligence-enabled smart driving cars, Internet-enabled medical equipment, etc. It is estimated that there will
be several dozens of billions of IoT devices ready and operating until 2020 around the world. Despite the
growing statistics, however, IoT has security vulnerabilities that must be addressed appropriately to avoid
causing damage in the future. As such, we mention some fields of study as a future topic at the end of the survey.
Consequently, in this comprehensive survey of IoT, we will cover the architecture of IoT with various layered
models, security characteristics, potential applications, and related supporting technologies of IoT such as 5G,
MEC, cloud, WSN, etc., including the economic perspective of IoT and its future directions.

Microblogging services (such as Twitter) are the representative information communication networks during
the Web 2.0 era, which have gained remarkable popularity. Weibo has become a popular platform for
information dissemination in online social networks due to its large number of users. In this study, a microblog
information dissemination model is presented. Related concepts are introduced and analyzed based on the
dynamic model of infectious disease, and new influencing factors are proposed to improve the susceptibleinfective-
removal (SIR) information dissemination model. Correlation analysis is conducted on the existing
information dissemination risk and the rumor dissemination model of microblog. In this study, web hyper is
used to model rumor dissemination. Finally, the experimental results illustrate the effectiveness of the method
in reducing the rumor dissemination of microblogs.

To figure out the impact of debt financing on the profits of industrial enterprises, it starts with calculating the
first differences against the logarithms of the cost profit ratios and the debt asset ratios of Chinese industrial
enterprises during 179 months from 2002 to 2016; next, it runs the cointegration test and afterwards the
regression test to analyze the obtained first differences, and still next uses the Simulink software to get the
regularity of those changes. It finds out that there is not only a long-term stable relationship between the
enterprises’ profits and debts, but also a steady time series trend within a short term. The profit rate positively
correlates to the debt asset ratio, and profit for the current term positively correlates to the profit for the
previous term. It indicates that properly raised debts can help increase the profit rate of the industrial
enterprises, and a higher previous profit level can help improve the current profit level.

Thanks to its potential in many applications, Blockchain has recently been nominated as one of the technologies exciting intense attention. Blockchain has solved the problem of changing the original low-trust centralized ledger held by a single third-party, to a high-trust decentralized form held by different entities, or in other words, verifying nodes. The key contribution of the work of Blockchain is the consensus algorithm, which decides how agreement is made to append a new block between all nodes in the verifying network. Blockchain algorithms can be categorized into two main groups. The first group is proof-based consensus, which requires the nodes joining the verifying network to show that they are more qualified than the others to do the appending work. The second group is voting-based consensus, which requires nodes in the network to exchange their results of verifying a new block or transaction, before making the final decision. In this paper, we present a review of the Blockchain consensus algorithms that have been researched and that are being applied in some well-known applications at this time

Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user’s comfort and improving the user’s experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively

With the rapid advancement of Internet services, there has been a dramatic increase in services that dynamically provide Internet resources on demand, such as cloud computing. In a cloud computing service, because the number of users in the cloud is changing dynamically, it is more efficient to utilize a flexible network technology such as software-defined networking (SDN). However, to efficiently support the SDNbased cloud computing service with limited resources, it is important to effectively manage the flow table at the SDN switch. Therefore, in this paper, a new flow management scheme is proposed that is able to, through efficient management, speed up the flow-entry search speed and simultaneously maximize the number of flow entries. The proposed scheme maximizes the capacity of the flow table by efficiently storing flow entry information while quickly executing the operation of flow-entry search by employing a hash index. In this paper, the proposed scheme is implemented by modifying the actual software SDN switch and then, its performance is analyzed. The results of the analysis show that the proposed scheme, by managing the flow tables efficiently, can support more flow entries

PCI Express (PCIe) bus, which was only used as an internal I/O bus of a computer system, has expanded its function to outside of a system, with progress of PCIe switching processor. In particular, advanced features of PCIe switching processor enable PCIe bus to serve as an interconnection network as well as connecting external devices. As PCIe switching processors more advanced, it is required to consider the different adapter card architecture. This study developed multipurpose adapter cards by applying an on-board optical module, a latest optical communications element, in order to improve transfer distance and utilization. The performance evaluation confirmed that the new adapter cards with long cable can provide the same bandwidth as that of the existing adapter cards with short copper cable.

Climate change has become a major challenge for sustainable development of human society. This study is an
attempt to analyze existing literature to identify economic indicators that hamper the process of global
warming. This paper includes case studies based on various countries to examine the nexus for environment
and its relationship with Foreign Direct Investment, transportation, economic growth and energy
consumption. Furthermore, the observations are analyzed from the perspective of China-Pakistan Economic
Corridor (CPEC) and probable impact on carbon emission of Pakistan. A major portion of CPEC investment is
allocated for transportation. However, it is evident that transportation sector is substantial emitter of carbon
dioxide (CO2) gas. Unfortunately, there is no empirical work on the subject of CPEC and carbon emission for
vehicular transportation. This paper infers that empirical results from various other countries are ambiguous
and inconclusive. Moreover, the evidence for the pollution haven hypothesis and the halo effect hypothesis is
limited in general and inapplicable for CPEC in particular. The major contribution of this study is the proposal
of an energy efficient transportation model for reducing CO2 emission. In the end, the paper suggests
strategies to climate researchers and policymakers for adaptation and mitigation of greenhouse gases (GHG).

We propose an enhanced version of the local binary pattern (LBP) operator for texture extraction in images in the context of image retrieval. The novelty of our proposal is based on the observation that the LBP exploits only the lowest kind of local information through the global histogram. However, such global Histograms reflect only the statistical distribution of the various LBP codes in the image. The block based LBP, which uses local histograms of the LBP, was one of few tentative to catch higher level textural information. We believe that important local and useful information in between the two levels is just ignored by the two schemas. The newly developed method: gradual locality integration of binary patterns (GLIBP) is a novel attempt to catch as much local information as possible, in a gradual fashion. Indeed, GLIBP aggregates the texture features present in grayscale images extracted by LBP through a complex structure. The used framework is comprised of a multitude of ellipse-shaped regions that are arranged in circular-concentric forms of increasing size. The framework of ellipses is in fact derived from a simple parameterized generator. In addition, the elliptic forms allow targeting texture directionality, which is a very useful property in texture characterization. In addition, the general framework of ellipses allows for taking into account the spatial information (specifically rotation). The effectiveness of GLIBP was investigated on the Corel-1K (Wang) dataset. It was also compared to published works including the very effective DLEP. Results show significant higher or comparable performance of GLIBP with regard to the other methods, which qualifies it as a good tool for scene images retrieval.

With the advent of the information society, image restoration technology has aroused considerable interest. Guided image filtering is more effective in suppressing noise in homogeneous regions, but its edge-preserving property is poor. As such, the critical part of guided filtering lies in the selection of the guided image. The result of the Expected Patch Log Likelihood (EPLL) method maintains a good structure, but it is easy to produce the ladder effect in homogeneous areas. According to the complementarity of EPLL with guided filtering, we propose a method of coupling EPLL and guided filtering for image de-noising. The EPLL model is adopted to construct the guided image for the guided filtering, which can provide better structural information for the guided filtering. Meanwhile, with the secondary smoothing of guided image filtering in image homogenization areas, we can improve the noise suppression effect in those areas while reducing the ladder effect brought about by the EPLL. The experimental results show that it not only retains the excellent performance of EPLL, but also produces better visual effects and a higher peak signal-to-noise ratio by adopting the proposed method.

The problem surrounding methods of implementing the software testing process has come under the
spotlight in recent times. However, as compliance with the software testing process does not necessarily bring
with it immediate economic benefits, IT companies need to pursue more aggressive efforts to improve the
process, and the software industry needs to makes every effort to improve the software testing process by
evaluating the Test Maturity Model integration (TMMi). Furthermore, as the software test process is only at
the initial level, high-quality software cannot be guaranteed. This paper applies TMMi model to Automobile
control software testing process, including test policy and strategy, test planning, test monitoring and control,
test design and execution, and test environment goal. The results suggest improvement of the automobile
control software testing process based on Test maturity model. As a result, this study suggest IT
organization’s test process improve method.

In efforts to increase its agricultural productivity, the Indonesian Center for Agricultural Biotechnology and
Genetic Resources Research and Development has conducted a variety of genomic studies using highthroughput
DNA genotyping and sequencing. The large quantity of data (big data) produced by these
biotechnologies require high performance data management system to store, backup, and secure data.
Additionally, these genetic studies are computationally demanding, requiring high performance processors
and memory for data processing and analysis. Reliable network connectivity with large bandwidth to transfer
data is essential as well as database applications and statistical tools that include cleaning, quality control,
querying based on specific criteria, and exporting to various formats that are important for generating high
yield varieties of crops and improving future agricultural strategies. This manuscript presents a reliable, secure,
and scalable information technology infrastructure tailored to Indonesian agriculture genotyping studies.

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact,
when a multi-branch company needs to explore its distributed data for decision making, it is imperative to
classify these multiple databases into similar clusters before analyzing the data. To search for the best
classification of a set of n databases, existing algorithms generate from 1 to (n2–n)/2 candidate classifications.
Although each candidate classification is included in the next one (i.e., clusters in the current classification are
subsets of clusters in the next classification), existing algorithms generate each classification independently,
that is, without taking into account the use of clusters from the previous classification. Consequently, existing
algorithms are time consuming, especially when the number of candidate classifications increases. To
overcome the latter problem, we propose in this paper an efficient approach that represents the problem of
classifying the multiple databases as a problem of identifying the connected components of an undirected
weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our
algorithm against existing works and that it overcomes the problem of increase in the execution time.

There are a great number of Internet-connected devices and their information can be acquired through an
Internet-wide scanning tool. By associating device information with publicly known security vulnerabilities,
security experts are able to determine whether a particular device is vulnerable. Currently, the identification
of the device information and its related vulnerabilities is manually carried out. It is necessary to automate the
process to identify a huge number of Internet-connected devices in order to analyze more than one hundred
thousand security vulnerabilities. In this paper, we propose a method of automatically generating device
information in the Common Platform Enumeration (CPE) format from banner text to discover potentially
weak devices having the Common Vulnerabilities Exposures (CVE) vulnerability. We demonstrated that our
proposed method can distinguish as much adequate CPE information as possible in the service banner.

The automatic extraction of temporal information from written texts is a key component of question
answering and summarization systems and its efficacy in those systems is very decisive if a temporal
expression (TE) is successfully extracted. In this paper, three different approaches for TE extraction in
Uyghur are developed and analyzed. A novel approach which uses lexical semantics as an additional
information is also presented to extend classical approaches which are mainly based on morphology and
syntax. We used a manually annotated news dataset labeled with TIMEX3 tags and generated three models
with different feature combinations. The experimental results show that the best run achieved 0.87 for
Precision, 0.89 for Recall, and 0.88 for F1-Measure in Uyghur TE extraction. From the analysis of the results,
we concluded that the application of semantic knowledge resolves ambiguity problem at shallower language
analysis and significantly aids the development of more efficient Uyghur TE extraction system.

Dynamic time warping (DTW) is the main algorithms for time series alignment. However, it is unsuitable for
quasi-periodic time series. In the current situation, except the recently published the shape exchange
algorithm (SEA) method and its derivatives, no other technique is able to handle alignment of this type of
very complex time series. In this work, we propose a novel algorithm that combines the advantages of the SEA
and the DTW methods. Our main contribution consists in the elevation of the DTW power of alignment
from the lowest level (Class A, non-periodic time series) to the highest level (Class C, multiple-periods time
series containing different number of periods each), according to the recent classification of time series
alignment methods proposed by Boucheham (Int J Mach Learn Cybern, vol. 4, no. 5, pp. 537-550, 2013). The
new method (quasi-periodic dynamic time warping [QP-DTW]) was compared to both SEA and DTW
methods on electrocardiogram (ECG) time series, selected from the Massachusetts Institute of Technology -
Beth Israel Hospital (MIT-BIH) public database and from the PTB Diagnostic ECG Database. Results show
that the proposed algorithm is more effective than DTW and SEA in terms of alignment accuracy on both
qualitative and quantitative levels. Therefore, QP-DTW would potentially be more suitable for many
applications related to time series (e.g., data mining, pattern recognition, search/retrieval, motif discovery,
classification, etc.).

For many years, matching in a bipartite graph has been widely used in various assignment problems, such as
stable marriage problem (SMP). As an application of bipartite matching, the problem of stable marriage is
defined over equally sized sets of men and women to identify a stable matching in which each person is
assigned a partner of opposite gender according to their preferences. The classical SMP proposed by Gale and
Shapley uses preference lists for each individual (men and women) which are infeasible in real world
applications for a large populace of men and women such as matrimonial websites. In this paper, we have
proposed an enhancement to the SMP by computing a weighted score for the users registered at matrimonial
websites. The proposed enhancement has been formulated into profit maximization of matrimonial websites
in terms of their ability to provide a suitable match for the users. The proposed formulation to maximize the
profits of matrimonial websites leads to a combinatorial optimization problem. We have proposed greedy and
genetic algorithm based approaches to solve the proposed optimization problem. We have shown that the
proposed genetic algorithm based approaches outperform the existing Gale-Shapley algorithm on the dataset
crawled from matrimonial websites.

A multiple classification system based on a new boosting technique has been approached utilizing different
biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting,
palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric
traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is
comprised of three different super-classifiers to individually perform person identification. The individual
classifiers corresponding to each super-classifier in their turn identify different biometric features and their
conclusions are integrated together in their respective super-classifiers. The decisions from individual superclassifiers
are integrated together through a mega-super-classifier to perform the final conclusion using
programming based boosting. The mega-super-classifier system using different super-classifiers in a compact
form is more reliable than single classifier or even single super-classifier system. The system has been
evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix
for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different
performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable.
Thereby making the system is efficient and effective.

Typical search engines may not be the most efficient means of returning images in accordance with user
requirements. With the help of semantic web technology, it is possible to search through images more
precisely in any required domain, because the images are annotated according to a custom-built ontology.
With appropriate annotations, a search can then, return images according to the context. This paper reports
on the design of a tourism ontology relevant to touristic images. In particular, the image features and the
meaning of the images are described using various properties, along with other types of information relevant
to tourist attractions using the OWL language. The methodology used is described, commencing with
building an image and tourism corpus, creating the ontology, and developing the search engine. The system
was tested through a case study involving the western region of Thailand. The user can search specifying the
specific class of image or they can use text-based searches. The results are ranked using weighted scores based
on kinds of properties. The precision and recall of the prototype system was measured to show its efficiency.
User satisfaction was also evaluated, was also performed and was found to be high.

Interval-valued neutrosophic hesitant fuzzy set (IVNHFS) is an extension of neutrosophic set (NS) and
hesitant fuzzy set (HFS), each element of which has truth membership hesitant function, indeterminacy
membership hesitant function and falsity membership hesitant function and the values of these functions lie
in several possible closed intervals in the real unit interval [0,1]. In contrast with NS and HFS, IVNHFS can be
more flexibly used to deal with uncertain, incomplete, indeterminate, inconsistent and hesitant information.
In this study, I propose the novel correlation coefficient of IVNHFSs and my paper discusses its properties.
Then, based on the novel correlation coefficient, I develop an approach to deal with multi-attribute decisionmaking
problems within the framework of IVNHFS. In the end, a practical example is used to show that the
approach is reasonable and effective in dealing with decision-making problems.

Nowadays, the number of pets in the Republic of Korea (ROK) is continuously growing, and people’s
perception of animals is changing. Accordingly, new systems and services for them are emerging. Despite
such changes, there are still many serious problems such as animal cruelty, abandonment, and factory-type
breeding places. In this study, we have conducted a research on the design of a humane animal care system
and its implementation with Java. The methodology involved in the design will enable managing animals'
safety and health by systematically categorizing and studying each health-related issue for protection.
Moreover, with this methodology, animals can avert risks through periodic examinations, and the analyzed
data will be useful in managing animals efficiently. Thus, this paper proposes a system that monitors whether
the owners actually carry out such obligation. Authors expect this convenient, easily accessible system to lead
to a more humane approach to the animals they own. The authors plan to establish an animal care network
together with local animal associations for the active promotion of the system implemented in this study, in
the hope that the network will be extended nationwide.

Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the
music improvisation process and can solve different optimization problems. In order to further improve the
performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters
including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are
optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved
method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four
complex function optimization and pressure vessel optimization problems were simulated using the
optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and
exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to
find global search and evolutionary speed. Optimization effect simulation results are satisfactory.

Video captioning refers to the process of extracting features from a video and generating video captions using
the extracted features. This paper introduces a deep neural network model and its learning method for
effective video captioning. In this study, visual features as well as semantic features, which effectively express
the video, are also used. The visual features of the video are extracted using convolutional neural networks,
such as C3D and ResNet, while the semantic features are extracted using a semantic feature extraction
network proposed in this paper. Further, an attention-based caption generation network is proposed for
effective generation of video captions using the extracted features. The performance and effectiveness of the
proposed model is verified through various experiments using two large-scale video benchmarks such as the
Microsoft Video Description (MSVD) and the Microsoft Research Video-To-Text (MSR-VTT).

Owing to limited energy in wireless devices power saving is very critical to prolong the lifetime of the
networks. In this regard, we designed a cross-layer optimization mechanism based on power control in which
source node broadcasts a Route Request Packet (RREQ) containing information such as node id, image size,
end to end bit error rate (BER) and residual battery energy to its neighbor nodes to initiate a multimedia
session. Each intermediate node appends its remaining battery energy, link gain, node id and average noise
power to the RREQ packet. Upon receiving the RREQ packets, the sink node finds node disjoint paths and
calculates the optimal power vectors for each disjoint path using cross layer optimization algorithm. Sink
based cross-layer maximal minimal residual energy (MMRE) algorithm finds the number of image packets
that can be sent on each path and sends the Route Reply Packet (RREP) to the source on each disjoint path
which contains the information such as optimal power vector, remaining battery energy vector and number of
packets that can be sent on the path by the source. Simulation results indicate that considerable energy saving
can be accomplished with the proposed cross layer power control algorithm.

Recently, Cyber Physical System (CPS) is one of the core technologies for realizing Internet of Things (IoT).
The CPS is a new paradigm that seeks to converge the physical and cyber worlds in which we live. However,
the CPS suffers from certain CPS issues that could directly threaten our lives, while the CPS environment,
including its various layers, is related to on-the-spot threats, making it necessary to study CPS security.
Therefore, a survey-based in-depth understanding of the vulnerabilities, threats, and attacks is required of
CPS security and privacy for IoT. In this paper, we analyze security issues, threats, and solutions for IoT-CPS,
and evaluate the existing researches. The CPS raises a number challenges through current security markets
and security issues. The study also addresses the CPS vulnerabilities and attacks and derives challenges.
Finally, we recommend solutions for each system of CPS security threats, and discuss ways of resolving
potential future issues.

An image fusion method is proposed on the basis of depth model segmentation to overcome the
shortcomings of noise interference and artifacts caused by infrared and visible image fusion. Firstly, the deep
Boltzmann machine is used to perform the priori learning of infrared and visible target and background
contour, and the depth segmentation model of the contour is constructed. The Split Bregman iterative
algorithm is employed to gain the optimal energy segmentation of infrared and visible image contours. Then,
the nonsubsampled contourlet transform (NSCT) transform is taken to decompose the source image, and the
corresponding rules are used to integrate the coefficients in the light of the segmented background contour.
Finally, the NSCT inverse transform is used to reconstruct the fused image. The simulation results of
MATLAB indicates that the proposed algorithm can obtain the fusion result of both target and background
contours effectively, with a high contrast and noise suppression in subjective evaluation as well as great merits
in objective quantitative indicators.

Recently, with the development of Internet technologies and propagation of smart devices, use of microblogs
such as Facebook, Twitter, and Instagram has been rapidly increasing. Many users check for new information
on microblogs because the content on their timelines is continually updating. Therefore, clustering algorithms
are necessary to arrange the content of microblogs by grouping them for a user who wants to get the newest
information. However, microblogs have word limits, and it has there is not enough information to analyze for
content clustering. In this paper, we propose a semantic-based K-means clustering algorithm that not only
measures the similarity between the data represented as a vector space model, but also measures the semantic
similarity between the data by exploiting the TagCluster for clustering. Through the experimental results on
the RepLab2013 Twitter dataset, we show the effectiveness of the semantic-based K-means clustering
algorithm.

Recently, computational intelligence has received a lot of attention from researchers due to its potential
applications to artificial intelligence. In computer science, computational intelligence refers to a machine’s
ability to learn how to compete various tasks, such as making observations or carrying out experiments. We
adopted a computational intelligence solution to monitoring residual resources in cloud computing environments.
The proposed residual resource monitoring scheme periodically monitors the cloud-based host machines, so
that the post migration performance of a virtual machine is as consistent with the pre-migration performance
as possible. To this end, we use a novel similarity measure to find the best target host to migrate a virtual
machine to. The design of the proposed residual resource monitoring scheme helps maintain the quality of
service and service level agreement during the migration. We carried out a number of experimental evaluations
to demonstrate the effectiveness of the proposed residual resource monitoring scheme. Our results show that
the proposed scheme intelligently measures the similarities between virtual machines in cloud computing
environments without causing performance degradation, whilst preserving the quality of service and service
level agreement.

Intelligent human identification using face information has been the research hotspot ranging from Internet
of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent
access control. Since 2D face images are usually captured from a long distance in an unconstrained environment,
to fully exploit this advantage and make human recognition appropriate for wider intelligent applications
with higher security and convenience, the key difficulties here include gray scale change caused by
illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by
pose or expression variation. To conquer these, many solutions have been proposed. However, most of them
only improve recognition performance under one influence factor, which still cannot meet the real face
recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture
to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted
on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed
algorithm exhibits excellent discriminative ability compared with other existing algorithms.

In this paper, we propose an improved model to provide users with a better long-term prediction of
waterworks operation data. The existing prediction models have been studied in various types of models such
as multiple linear regression model while considering time, days and seasonal characteristics. But the existing
model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient.
Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to
predict data of water purification plant because its time series prediction is highly reliable. However, it is
necessary to reflect the correlation among various related factors, and a supplementary model is needed to
improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced
to select various input variables that have a necessary correlation and to improve long term prediction rate,
thus increasing the prediction rate through the LSTM predictive value and the combined structure. In
addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM,
which then confirms the data as the final predicted outcome.

The discrete wavelet transform (DWT) has good multi-resolution decomposition characteristic and its low frequency component contains the basic information of an image. Based on this, a fragile watermarking using the local binary pattern (LBP) and DWT is proposed for image authentication. In this method, the LBP pattern of low frequency wavelet coefficients is adopted as a feature watermark, and it is inserted into the least significant bit (LSB) of the maximum pixel value in each block of host image. To guarantee the safety of the proposed algorithm, the logistic map is applied to encrypt the watermark. In addition, the locations of the maximum pixel values are stored in advance, which will be used to extract watermark on the receiving side. Due to the use of DWT, the watermarked image generated by the proposed scheme has high visual quality. Compared with other state-of-the-art watermarking methods, experimental results manifest that the proposed algorithm not only has lower watermark payloads, but also achieves good performance in tamper identification and localization for various attacks.

With increasing interests in renewables, more consumers are installing an energy storage system (ESS) in their backyards, and thus, the ESS will play a critical role in the emerging smart grid. Due to mechanical properties, however its operational dynamics must be well understood before connecting the ESS to the smart grid (and eventually to an IT system). To this end, we investigate charging and discharging processes in detail. This paper, then, proposes methods for four type tests (state of charge test, conversion efficiency test, response time test, and ramp rate test) that can assess the dynamics of the ESS. The proposed methods can capture accurate delay values of mechanical processes in the ESS, and it is expected for those values to help design real-time communication systems in the smart grid involving the ESS.

One of the most visible developments in Decision Support Systems (DSS) was the emergence of rule-based expert systems. Hence, despite their success in many sectors, developers of Medical Rule-Based Systems have met several critical problems. Firstly, the rules are related to a clearly stated subject. Secondly, a rule-based system can only learn by updating of its rule-base, since it requires explicit knowledge of the used domain. Solutions to these problems have been sought through improved techniques and tools, improved development paradigms, knowledge modeling languages and ontology, as well as advanced reasoning techniques such as case-based reasoning (CBR) which is well suited to provide decision support in the healthcare setting. However, using CBR reveals some drawbacks, mainly in its interrelated tasks: the retrieval and the adaptation. For the retrieval task, a major drawback raises when several similar cases are found and consequently several solutions. Hence, a choice for the best solution must be done. To overcome these limitations, numerous useful works related to the retrieval task were conducted with simple and convenient procedures or by combining CBR with other techniques. Through this paper, we provide a combining approach using the multi-criteria analysis (MCA) to help, the traditional retrieval task of CBR, in choosing the best solution. Afterwards, we integrate this approach in a decision model to support medical decision. We present, also, some preliminary results and suggestions to extend our approach.

The network coding mechanism has attracted much attention because of its advantage of enhanced network throughput which is a desirable characteristic especially in a multi-hop wireless network with limited link capacity such as the device-to-device (D2D) communication network of 5G. COPE proposes to use the XOR- based network coding in the two-hop wireless network topology. For multi-hop wireless networks, the Distributed Coding-Aware Routing (DCAR) mechanism was proposed, in which the coding conditions for two flows intersecting at an intermediate node are defined and the routing metric to improve the coding opportunity by preferring those routes with longer queues is designed. Because the routes with longer queues may increase the delay, DCAR is inefficient in delivering real-time multimedia traffic flows. In this paper, we propose a network coding-aware routing protocol for multi-hop wireless networks that enhances DCAR by considering traffic load distribution and link quality. From this, we can achieve higher network throughput and lower end-to-end delay at the same time for the proper delivery of time-sensitive data flow. The Qualnet-based simulation results show that our proposed scheme outperforms DCAR in terms of throughput and delay.

The handwriting based person identification systems use their designer’s perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer’s personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.

The Journal of Information Processing Systems (JIPS) is the official international journal published by the Korean Information Processing Society. As a leading and multidisciplinary journal, JIPS is indexed in ESCI, SCOPUS, EI COMPENDEX, DOI, DBLP, EBSCO, Google Scholar and CrossRef. Its purpose is to enable researchers and professionals to promote, share, and discuss all major research issues and developments in the field of information processing technologies and other related fields. JIPS publishes diverse papers, including theoretical research contributions presenting new techniques, concepts, or analyses; experience reports; experiments involving the implementation and application of new theories; and tutorials on state-of-the-art technologies related to information processing systems. The subjects covered by this journal include, but are not limited to, topics related to computer systems and theories, multimedia systems and graphics, communication systems and security, and software systems and applications.

This survey paper explores the application of multimodal feedback in automated systems for motor learning. In this paper, we review the findings shown in recent studies in this field using rehabilitation and various motor training scenarios as context. We discuss popular feedback delivery and sensing mechanisms for motion capture and processing in terms of requirements, benefits, and limitations. The selection of modalities is presented via our having reviewed the best-practice approaches for each modality relative to motor task complexity with example implementations in recent work. We summarize the advantages and disadvantages of several approaches for integrating modalities in terms of fusion and frequency of feedback during motor tasks. Finally, we review the limitations of perceptual bandwidth and provide an evaluation of the information transfer for each modality.

The wireless sensor networks (WSNs) became a very essential tool in borders and military zones surveillance, for this reason specific applications have been developed. Surveillance is usually accomplished through the deployment of nodes in a random way providing heterogeneous topologies. However, the process of the identification of all nodes located on the network’s outer edge is very long and energy-consuming. Before any other activities on such sensitive networks, we have to identify the border nodes by means of specific algorithms. In this paper, a solution is proposed to solve the problem of energy and time consumption in detecting border nodes by means of node selection. This mechanism is designed with several starter nodes in order to reduce time, number of exchanged packets and then, energy consumption. This method consists of three phases: the first one is to detect triggers which serve to start the mechanism of boundary nodes (BNs) detection, the second is to detect the whole border, and the third is to exclude each BN from the routing tables of all its neighbors so that it cannot be used for the routing.

Mobile phones are the most common communication devices in history. For this reason, the number of mobile subscribers will increase dramatically in the future. Therefore, the determining the location of a mobile station will become more and more difficult. The mobile station must be authenticated to inform the network of its current location even when the user switches it on or when its location is changed. The most basic weakness in the GSM authentication protocol is the unilateral authentication process where the customer is verified by the system, yet the system is not confirmed by the customer. This creates numerous security issues, including powerlessness against man-in-the-middle attacks, vast bandwidth consumption between VLR and HLR, storage space overhead in VLR, and computation costs in VLR and HLR. In this paper, we propose a secure authentication mechanism based new mobility management method to improve the location management in the GSM network, which suffers from a lot off drawbacks, such as transmission cost and database overload. Numerical analysis is done for both conventional and modified versions and compared together. The numerical results show that our protocol scheme is more secure and that it reduces mobility management costs the most in the GSM network.

Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.

Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification

In this paper, an interference aware distributed multi-channel MAC (IDMMAC) protocol is proposed for wireless sensor and actor networks (WSANs). The WSAN consists of a huge number of sensors and ample amount of actors. Hence, in the IDMMAC protocol a lightweight channel selection mechanism is proposed to enhance the sensor's lifetime. The IDMMAC protocol divides the beacon interval into two phases (i.e., the ad- hoc traffic indication message (ATIM) window phase and data transmission phase). When a sensor wants to transmit event information to the actor, it negotiates the maximum packet reception ratio (PRR) and the capacity channel in the ATIM window with its 1-hop sensors. The channel negotiation takes place via a control channel. To improve the packet delivery ratio of the IDMMAC protocol, each actor selects a backup cluster head (BCH) from its cluster members. The BCH is elected based on its residual energy and node degree. The BCH selection phase takes place whenever an actor wants to perform actions in the event area or it leaves the cluster to help a neighbor actor. Furthermore, an interference and throughput aware multi- channel MAC protocol is also proposed for actor-actor coordination. An actor selects a minimum interference and maximum throughput channel among the available channels to communicate with the destination actor. The performance of the proposed IDMMAC protocol is analyzed using standard network parameters, such as packet delivery ratio, end-to-end delay, and energy dissipation, in the network. The obtained simulation results indicate that the IDMMAC protocol performs well compared to the existing MAC protocols.

The dorsal hand vein biometric system developed has a main objective and specific targets; to get an electronic signature using a secure signature device. In this paper, we present our signature device with its different aims; respectively: The extraction of the dorsal veins from the images that were acquired through an infrared device. For each identification, we need the representation of the veins in the form of shape descriptors, which are invariant to translation, rotation and scaling; this extracted descriptor vector is the input of the matching step. The optimization decision system settings match the choice of threshold that allows accepting/rejecting a person, and selection of the most relevant descriptors, to minimize both FAR and FRR errors. The final decision for identification based descriptors selected by the PSO hybrid binary give a FAR =0% and FRR=0% as results.

A watermark is a signal added to the original signal in order to preserve the copyright of the owner of the digital content. The basic challenge for designing a watermarking system is a dilemma between transparency and robustness. If we want a higher rate of transparency, there has to be a compromise in terms of its robustness and vice versa. Also, until now, watermarking is generalized, resulting in the need for a specialized algorithm to work for a specialized image processing application domain. Our proposed technique takes into consideration the image characteristics for watermark insertion and it optimizes transparency and robustness. It achieved a 99.98% retrieval efficiency for an image blurring attack and counterfeits other attacks. Our proposed technique counterfeits almost all of the image processing attacks.

Image restoration has been carried out by texture synthesis mostly for large regions and inpainting algorithms for small cracks in images. In this paper, we propose a new approach that allows for the simultaneous fill-in of different structures and textures by processing in a wavelet domain. A combination of structure inpainting and patch-based texture synthesis is carried out, which is known as patch-based inpainting, for filling and updating the target region. The wavelet transform is used for its very good multiresolution capabilities. The proposed algorithm uses the wavelet domain subbands to resolve the structure and texture components in smooth approximation and high frequency structural details. The subbands are processed separately by the prioritized patch-based inpainting with isophote energy driven texture synthesis at the core. The algorithm automatically estimates the wavelet coefficients of the target regions of various subbands using optimized patches from the surrounding DWT coefficients. The suggested performance improvement drastically improves execution speed over the existing algorithm. The proposed patch optimization strategy improves the quality of the fill. The fill-in is done with higher priority to structures and isophotes arriving at target boundaries. The effectiveness of the algorithm is demonstrated with natural and textured images with varying textural complexions.

Nowadays most vehicles are equipped with a variety of electronic devices to improve user convenience as well as its performance itself. In order to efficiently interconnect these devices with each other, Controller Area Network (CAN) is commonly used. However, the CAN requires reconfiguration of the entire network when a new device, which is capable of supporting both of transmission and reception of data, is added to the existing network. In addition, since CAN is based on the collision avoidance using address priority, it is difficult that a new node is assigned high priority and eventually it results in transmission delay of the entire network. Therefore, in this paper we propose a new system component, called CAN coordinator, and design a new CAN framework capable of supporting plug and play functionality. Through experiments, we also prove that the proposed framework can improve real-time ability based on plug and play functionality.

The image segmentation is the most important operation in an image processing system. It is located at the joint between the processing and analysis of the images. Unsupervised segmentation aims to automatically separate the image into natural clusters. However, because of its complexity several methods have been proposed, specifically methods of optimization. In our work we are interested to the technique SFLA (Shuffled Frog-Leaping Algorithm). It’s a memetic meta-heuristic algorithm that is based on frog populations in nature searching for food. This paper proposes a new approach of unsupervised image segmentation based on SFLA method. It is implemented and applied to different types of images. To validate the performances of our approach, we performed experiments which were compared to the method of K-means.

Nowadays, with the development of signal processing technique, the protection to the integrity and authenticity of images has become a topic of great concern. A blind image authentication technology with high tamper detection accuracy for different common attacks is urgently needed. In this paper, an improved fragile watermarking method based on local binary pattern (LBP) is presented for blind tamper location in images. In this method, a binary watermark is generated by LBP operator which is often utilized in face identification and texture analysis. In order to guarantee the safety of the proposed algorithm, Arnold transform and logistic map are used to scramble the authentication watermark. Then, the least significant bits (LSBs) of original pixels are substituted by the encrypted watermark. Since the authentication data is constructed from the image itself, no original image is needed in tamper detection. The LBP map of watermarked image is compared to the extracted authentication data to determine whether it is tampered or not. In comparison with other state-of-the-art schemes, various experiments prove that the proposed algorithm achieves better performance in forgery detection and location for baleful attacks.

Associative and bidirectional associative memories are examples of associative structures studied intensively in the literature. The underlying idea is to realize associative mapping so that the recall processes (one- directional and bidirectional ones) are realized with minimal recall errors. Associative and fuzzy associative memories have been studied in numerous areas yielding efficient applications for image recall and enhancements and fuzzy controllers, which can be regarded as one-directional associative memories. In this study, we revisit and augment the concept of associative memories by offering some new design insights where the corresponding mappings are realized on the basis of a related collection of landmarks (prototypes) over which an associative mapping becomes spanned. In light of the bidirectional character of mappings, we have developed an augmentation of the existing fuzzy clustering (fuzzy c-means, FCM) in the form of a so- called collaborative fuzzy clustering. Here, an interaction in the formation of prototypes is optimized so that the bidirectional recall errors can be minimized. Furthermore, we generalized the mapping into its granular version in which numeric prototypes that are formed through the clustering process are made granular so that the quality of the recall can be quantified. We propose several scenarios in which the allocation of information granularity is aimed at the optimization of the characteristics of recalled results (information granules) that are quantified in terms of coverage and specificity. We also introduce various architectural augmentations of the associative structures.

Multimedia is a ubiquitous and indispensable part of our daily life and learning such as audio, image, and video. Objective and subjective quality evaluations play an important role in various multimedia applications. Blind image quality assessment (BIQA) is used to indicate the perceptual quality of a distorted image, while its reference image is not considered and used. Blur is one of the common image distortions. In this paper, we propose a novel BIQA index for Gaussian blur distortion based on the fact that images with different blur degree will have different changes through the same blur. We describe this discrimination from three aspects: color, edge, and structure. For color, we adopt color histogram; for edge, we use edge intensity map, and saliency map is used as the weighting function to be consistent with human visual system (HVS); for structure, we use structure tensor and structural similarity (SSIM) index. Numerous experiments based on four benchmark databases show that our proposed index is highly consistent with the subjective quality assessment.

Cloud computing is an attractive solution that can provide low cost storage and powerful processing capabilities for government agencies or enterprises of small and medium size. Yet the confidentiality of information should be considered by any organization migrating to cloud, which makes the research on relational database system based on encryption schemes to preserve the integrity and confidentiality of data in cloud be an interesting subject. So far there have been various solutions for realizing SQL queries on encrypted data in cloud without decryption in advance, where generally homomorphic encryption algorithm is applied to support queries with aggregate functions or numerical computation. But the existing homomorphic encryption algorithms cannot encrypt floating-point numbers. So in this paper, we present a mechanism to enable the trusted party to encrypt the floating-points by homomorphic encryption algorithm and partial trusty server to perform summation on their ciphertexts without revealing the data itself. In the first step, we encode floating-point numbers to hide the decimal points and the positive or negative signs. Then, the codes of floating-point numbers are encrypted by homomorphic encryption algorithm and stored as sequences in cloud. Finally, we use the data structure of DoubleListTree to implement the aggregate function of SUM and later do some extra processes to accomplish the summation

Due to the rapid growth and expansion of the Internet, the digital multimedia such as image, audio and video are available for everyone. Anyone can make unauthorized copying for any digital product. Accordingly, the owner of these products cannot protect his ownership. Unfortunately, this situation will restrict any improvement which can be done on the digital media production in the future. Some procedures have been proposed to protect these products such as cryptography and watermarking techniques. Watermarking means embedding a message such as text, the image is called watermark, yet, in a host such as a text, an image, an audio, or a video, it is called a cover. Watermarking can provide and ensure security, data authentication and copyright protection for the digital media. In this paper, a new watermarking method of still image is proposed for the purpose of copyright protection. The procedure of embedding watermark is done in a transform domain. The discrete cosine transform (DCT) is exploited in the proposed method, where the watermark is embedded in the selected coefficients according to several criteria. With this procedure, the deterioration on the image is minimized to achieve high invisibility. Unlike the traditional techniques, in this paper, a new method is suggested for selecting the best blocks of DCT coefficients. After selecting the best DCT coefficients blocks, the best coefficients in the selected blocks are selected as a host in which the watermark bit is embedded. The coefficients selection is done depending on a weighting function method, where this function exploits the values and locations of the selected coefficients for choosing them. The experimental results proved that the proposed method has produced good imperceptibility and robustness for different types of attacks

In this paper, we analyze a recently proposed semi-fragile watermarking scheme based on local binary pattern (LBP) operators, and note that it has a fundamental flaw in the design. In this work, a binary watermark is embedded into image blocks by modifying the neighborhood pixels according to the LBP pattern. However, different image blocks might have the same LBP pattern, which can lead to false detection in watermark extraction process. In other words, one can modify the host image intentionally without affecting its watermark message. In addition, there is no encryption process before watermark embedding, which brings another potential security problem. To illustrate its weakness, two special copy-paste attacks are proposed in this paper, and several experiments are conducted to prove the effectiveness of these attacks. To solve these problems, an improved semi-fragile watermarking based on LBP operators is presented. In watermark embedding process, the central pixel value of each block is taken into account and Arnold transform is adopted to guarantee the security of watermark. Experimental results show that the improved watermarking scheme can overcome the above defects and locate the tampered region effectively.

Recently, the importance of big data has been emphasized with the development of smartphone, web/SNS. As a result, MapReduce, which can efficiently process big data, is receiving worldwide attention because of its excellent scalability and stability. Since big data has a large amount, fast creation speed, and various properties, it is more efficient to process big data summary information than big data itself. Wavelet histogram, which is a typical data summary information generation technique, can generate optimal data summary information that does not cause loss of information of original data. Therefore, a system applying a wavelet histogram generation technique based on MapReduce has been actively studied. However, existing research has a disadvantage in that the generation speed is slow because the wavelet histogram is generated through one or more MapReduce Jobs. And there is a high possibility that the error of the data restored by the wavelet histogram becomes large. However, since the wavelet histogram generation system based on the MapReduce developed in this paper generates the wavelet histogram through one MapReduce Job, the generation speed can be greatly increased. In addition, since the wavelet histogram is generated by adjusting the error boundary specified by the user, the error of the restored data can be adjusted from the wavelet histogram. Finally, we verified the efficiency of the wavelet histogram generation system developed in this paper through performance evaluation.

This paper proposes an automatic method to summarize Bangla news document. In the proposed approach, pronoun replacement is accomplished for the first time to minimize the dangling pronoun from summary. After replacing pronoun, sentences are ranked using term frequency, sentence frequency, numerical figures and title words. If two sentences have at least 60% cosine similarity, the frequency of the larger sentence is increased, and the smaller sentence is removed to eliminate redundancy. Moreover, the first sentence is included in summary always if it contains any title word. In Bangla text, numerical figures can be presented both in words and digits with a variety of forms. All these forms are identified to assess the importance of sentences. We have used the rule-based system in this approach with hidden Markov model and Markov chain model. To explore the rules, we have analyzed 3,000 Bangla news documents and studied some Bangla grammar books. A series of experiments are performed on 200 Bangla news documents and 600 summaries (3 summaries are for each document). The evaluation results demonstrate the effectiveness of the proposed technique over the four latest methods.

This paper introduces a new algorithm that renders motion blur using triangular motion paths. A triangle occupies a set of pixels when moving from a position in the start of a frame to another position in the end of a frame. This is a motion path of a moving triangle. For a given pixel, we use a motion path of each moving triangle to find a range of time that this moving triangle is visible to the camera. Then, we sort visible time ranges in the depth-time dimensions and use bitwise operations to solve the occlusion problem. Thereafter, we compute an average color of each moving triangle based on its visible time range. Finally, we accumulate an average color of each moving triangle in the front-to-back order to produce the final pixel color. Thus, our algorithm performs shading after the visibility test and renders motion blur in real time.

The round robin algorithm is regarded as one of the most efficient and effective CPU scheduling techniques in computing. It centres on the processing time required for a CPU to execute available jobs. Although there are other CPU scheduling algorithms based on processing time which use different criteria, the round robin algorithm has gained much popularity due to its optimal time-shared environment. The effectiveness of this algorithm depends strongly on the choice of time quantum. This paper presents a new effective round robin CPU scheduling algorithm. The effectiveness here lies in the fact that the proposed algorithm depends on a dynamically allocated time quantum in each round. Its performance is compared with both traditional and enhanced round robin algorithms, and the findings demonstrate an improved performance in terms of average waiting time, average turnaround time and context switching.

Social networking services (SNSs) such as Twitter, MySpace, and Facebook have become progressively significant with its billions of users. Still, alongside this increase is an increase in security threats such as cross- site scripting (XSS) threat. Recently, a few approaches have been proposed to detect an XSS attack on SNSs. Due to the certain recent features of SNSs webpages such as JavaScript and AJAX, however, the existing approaches are not efficient in combating XSS attack on SNSs. In this paper, we propose a machine learning- based approach to detecting XSS attack on SNSs. In our approach, the detection of XSS attack is performed based on three features: URLs, webpage, and SNSs. A dataset is prepared by collecting 1,000 SNSs webpages and extracting the features from these webpages. Ten different machine learning classifiers are used on a prepared dataset to classify webpages into two categories: XSS or non-XSS. To validate the efficiency of the proposed approach, we evaluated and compared it with other existing approaches. The evaluation results show that our approach attains better performance in the SNS environment, recording the highest accuracy of 0.972 and lowest false positive rate of 0.87.

This paper aims to extract an ObjectProperty-UsageMethod relation, in particular the HerbalMedicinalProperty- UsageMethod relation of the herb-plant object, as a semantic relation between two related sets, a herbal- medicinal-property concept set and a usage-method concept set from several web documents. This HerbalMedicinalProperty-UsageMethod relation benefits people by providing an alternative treatment/solution knowledge to health problems. The research includes three main problems: how to determine EDU (where EDU is an elementary discourse unit or a simple sentence/clause) with a medicinal-property/usage-method concept; how to determine the usage-method boundary; and how to determine the HerbalMedicinalProperty- UsageMethod relation between the two related sets. We propose using N-Word-Co on the verb phrase with the medicinal-property/usage-method concept to solve the first and second problems where the N-Word-Co size is determined by the learning of maximum entropy, support vector machine, and nai?ve Bayes. We also apply nai?ve Bayes to solve the third problem of determining the HerbalMedicinalProperty-UsageMethod relation with N-Word-Co elements as features. The research results can provide high precision in the HerbalMedicinalProperty-UsageMethod relation extraction.

In the past decades, various image regularization methods have been introduced. Among them, total variation model has drawn much attention for the reason of its low computational complexity and well-understood mathematical behavior. However, regularization parameter estimation of total variation model is still an open problem. To deal with this problem, a novel adaptive regularization parameter selection scheme is proposed in this paper, by means of using the local spectral response, which has the capability of locally selecting the regularization parameters in a content-aware way and therefore adaptively adjusting the weights between the two terms of the total variation model. Experiment results on simulated and real noisy image show the good performance of our proposed method, in visual improvement and peak signal to noise ratio value.

A new medical materials scheduling system and its modeling method for the complex rescue are presented. Different from other similar system, first both the BeiDou Satellite Communication System (BSCS) and the Special Fiber-optic Communication Network (SFCN) are used to collect the rescue requirements and the location information of disaster areas. Then all these messages will be displayed in a special medical software terminal. After that the bipartite graph models are utilized to compute the optimal scheduling of medical materials. Finally, all these results will be transmitted back by the BSCS and the SFCN again to implement a fast guidance of medical rescue. The sole drug scheduling issue, the multiple drugs scheduling issue, and the backup-scheme selection issue are all utilized: the Kuhn-Munkres algorithm is used to realize the optimal matching of sole drug scheduling issue, the spectral clustering-based method is employed to calculate the optimal distribution of multiple drugs scheduling issue, and the similarity metric of neighboring matrix is utilized to realize the estimation of backup-scheme selection issue of medical materials. Many simulation analysis experiments and applications have proved the correctness of proposed technique and system.

Related to the maximum vector problem, a skyline query is to discover dominating tuples from a set of tuples, where each defines an object (such as a hotel) in several dimensions (such as the price and the distance to the beach). A tuple, an instance of an object, dominates another tuple if it is equally good or better in all dimensions and better in at least one dimension. Traditionally, skyline queries are defined upon single- instance data or upon objects each of which is associated with an instance. However, in some cases, an object is not associated with a single instance but rather by multiple instances. For example, on a review website, many users assign scores to a product or a service, and a user’s score is an instance of the object representing the product or the service. Such data is an example of multi-instance data. Unlike most (if not all) others considering the traditional setting, we consider skyline queries defined upon multi-instance data. We define the dominance calculation and propose an algorithm to reduce its computational cost. We use synthetic and real data to evaluate the proposed methods, and the results demonstrate their utility.

For text categorization task, distinctive text features selection is important due to feature space high dimensionality. It is important to decrease the feature space dimension to decrease processing time and increase accuracy. In the current study, for text categorization task, we introduce a novel statistical feature selection approach. This approach measures the term distribution in all collection documents, the term distribution in a certain category and the term distribution in a certain class relative to other classes. The proposed method results show its superiority over the traditional feature selection methods.

Mobility arises naturally in the Internet of Things networks, since the location of mobile objects, e.g., mobile agents, mobile software, mobile things, or users with wireless hardware, changes as they move. Tracking their current location is essential to mobile computing. To overcome the scalability problem, hierarchical architectures of location databases have been proposed. When location updates and lookups for mobile objects are localized, these architectures become effective. However, the network signaling costs and the execution number of database operations increase particularly when the scale of the architectures and the numbers of databases becomes large to accommodate a great number of objects. This disadvantage can be alleviated by a location caching scheme which exploits the spatial and temporal locality in location lookup. In this paper, we propose a hierarchical location caching scheme, which acclimates the existing location caching scheme to a hierarchical architecture of location databases. The performance analysis indicates that the adjustment of such thresholds has an impact on cost reduction in the proposed scheme.

Quorum-based algorithms are widely used for solving several problems in mobile ad hoc networks (MANET) and wireless sensor networks (WSN). Several quorum-based protocols are proposed for multi-hop ad hoc networks that each one has its pros and cons. Quorum-based protocol (QEC or QPS) is the first study in the asynchronous sleep scheduling protocols. At the time, most of the proposed protocols were non-adaptive ones. But nowadays, adaptive quorum-based protocols have gained increasing attention, because we need protocols which can change their quorum size adaptively with network conditions. In this paper, we first introduce the most popular quorum systems and explain quorum system properties and its performance criteria. Then, we present a comparative and comprehensive survey of the non-adaptive and adaptive quorum-based protocols which are subsequently discussed in depth. We also present the comparison of different quorum systems in terms of the expected quorum overlap size (EQOS) and active ratio. Finally, we summarize the pros and cons of current adaptive and non-adaptive quorum-based protocols.

The Active Appearance Model (AAM) is a class of deformable models, which, in the segmentation process, integrates the priori knowledge on the shape and the texture and deformation of the structures studied. This model in its sequential form is computationally intensive and operates on large data sets. This paper presents another framework to implement the standard version of the AAM model. We suggest a distributed and parallel approach justified by the characteristics of the model and their potentialities. We introduce a schema for the representation of the overall model and we study of operations that can be parallelized. This approach is intended to exploit the benefits build in the area of advanced image processing.

Organizations in some industries are still hesitant to adopt the Enterprise Resource Planning (ERP) system due to its high risk of failures. This study examined how industry classification affects the successful implementation of the ERP system. To achieve this goal, we reinvestigated the existing ERP Success Model that was developed by Chung with the data from various industry sectors, since Chung validated the model only in the engineering and construction industries. In order to test to see if the Chung model can be applicable outside the engineering and construction industries, the relationships between the ERP success indicators and the critical success factors in the Chung model and those in the sample data collected from ten different industry sectors were compared and investigated. The ten industry sectors were selected based on the Global Industry Classification Standard (GICS). We found that the impact of success factors on the success of implementing an ERP system varied across industry sectors. This means that the success of ERP system implementation can be industry-specific. Thus, industry classification should be considered as another factor to help IT decision makers or top-management avoid ERP system failures when they plan to implement a new ERP system.

This paper presents a complete method for vehicle detection and tracking in a fixed setting based on computer vision. Vehicle detection is performed based on Scale Invariant Feature Transform (SIFT) feature matching. With SIFT feature detection and matching, the geometrical relations between the two images is estimated. Then, the previous image is aligned with the current image so that moving vehicles can be detected by analyzing the difference image of the two aligned images. Vehicle tracking is also performed based on SIFT feature matching. For the decreasing of time consumption and maintaining higher tracking accuracy, the detected candidate vehicle in the current image is matched with the vehicle sample in the tracking sample set, which contains all of the detected vehicles in previous images. Most remarkably, the management of vehicle entries and exits is realized based on SIFT feature matching with an efficient update mechanism of the tracking sample set. This entire method is proposed for highway traffic environment where there are no non- automotive vehicles or pedestrians, as these would interfere with the results.

Global value numbering (GVN) is a method for detecting equivalent expressions in programs. Most of the GVN algorithms concentrate on detecting equalities among variables and hence, are limited in their ability to identify value-based redundancies. In this paper, we suggest improvements by which the efficient GVN algo- rithm by Gulwani and Necula (2007) can be made to detect expression equivalences that are required for identifying value based redundancies. The basic idea for doing so is to use an anticipability-based Join algo- rithm to compute more precise equivalence information at join points. We provide a proof of correctness of the improved algorithm and show that its running time is a polynomial in the number of expressions in the program

Cloud computing is a distributed computing model that has lot of drawbacks and faces difficulties. Many new innovative and emerging techniques take advantage of its features. In this paper, we explore the security threats to and Risk Assessments for cloud computing, attack mitigation frameworks, and the risk-based dynamic access control for cloud computing. Common security threats to cloud computing have been explored and these threats are addressed through acceptable measures via governance and effective risk management using a tailored Security Risk Approach. Most existing Threat and Risk Assessment (TRA) schemes for cloud services use a converse thinking approach to develop theoretical solutions for minimizing the risk of security breaches at a minimal cost. In our study, we propose an improved Attack-Defense Tree mechanism designated as iADTree, for solving the TRA problem in cloud computing environments.

The exceptional development of electronic device technology, the miniaturization of mobile devices, and the development of telecommunication technology has made it possible to monitor human biometric data anywhere and anytime by using different types of wearable or embedded sensors. In daily life, mobile devices can collect wireless body area network (WBAN) data, and the co-collected location data is also important for disease analysis. In order to efficiently analyze WBAN data, including location information and support medical analysis services, we propose a geohash-based spatial index method for a location-aware WBAN data monitoring system on the NoSQL database system, which uses an R-tree-based global tree to organize the real-time location data of a patient and a B-tree-based local tree to manage historical data. This type of spatial index method is a support cloud-based location-aware WBAN data monitoring system. In order to evaluate the proposed method, we built a system that can support a JavaScript Object Notation (JSON) and Binary JSON (BSON) document data on mobile gateway devices. The proposed spatial index method can efficiently process location-based queries for medical signal monitoring. In order to evaluate our index method, we simulated a small system on MongoDB with our proposed index method, which is a document-based NoSQL database system, and evaluated its performance.

A primary task in wireless sensor networks (WSNs) is data collection. The main objective of this task is to collect sensor readings from sensor fields at predetermined sinks using routing protocols without conducting network processing at intermediate nodes, which have been proved as being inefficient in many research studies using a static sink. The major drawback is that sensor nodes near a data sink are prone to dissipate more energy power than those far away due to their role as relay nodes. Recently, novel WSN architectures based on mobile sinks and mobile relay nodes, which are able to move inside the region of a deployed WSN, which has been developed in most research works related to mobile WSN mainly exploit mobility to reduce and balance energy consumption to enhance communication reliability among sensor nodes. Our main purpose in this paper is to propose a solution to the problem of deploying mobile data collectors for alleviating the high traffic load and resulting bottleneck in a sink’s vicinity, which are caused by static approaches. For this reason, several WSNs based on mobile elements have been proposed. We studied two key issues in WSN mobility: the impact of the mobile element (sink or relay nodes) and the impact of the mobility model on WSN based on its performance expressed in terms of energy efficiency and reliability. We conducted an extensive set of simulation experiments. The results obtained reveal that the collection approach based on relay nodes and the mobility model based on stochastic perform better.

TCS_SHA-3 is a family of four cryptographic hash functions that are covered by a United States patent (US 2009/0262925). The digest sizes are 224, 256, 384 and 512 bits. The hash functions use bijective functions in place of the standard compression functions. In this paper we describe first and second preimage attacks on the full hash functions. The second preimage attack requires negligible time and the first preimage attack requires O(236) time. In addition to these attacks, we also present a negligible time second preimage attack on a strengthened variant of the TCS_SHA-3. All the attacks have negligible memory requirements. To the best of our knowledge, there is no prior cryptanalysis of any member of the TCS_SHA-3 family in the literature.

In order to considerably reduce the ambiguity rate, we propose in this article a disambiguation approach that is based on the selection of the right diacritics at different analysis levels. This hybrid approach combines a linguistic approach with a multi-criteria decision one and could be considered as an alternative choice to solve the morpho-lexical ambiguity problem regardless of the diacritics rate of the processed text. As to its evaluation, we tried the disambiguation on the online Alkhalil morphological analyzer (the proposed approach can be used on any morphological analyzer of the Arabic language) and obtained encouraging results with an F-measure of more than 80%.

As interest in the Internet increases, related technologies are also quickly progressing. As smart devices become more widely used, interest is growing in words are missing here like “improving the” or “figuring out how to use the” future Internet to resolve the fundamental issues of transmission quality and security. The future Internet is being studied to improve the limits of existing Internet structures and to reflect new requirements. In particular, research on words are missing here like “finding new forms of” or “applying new forms of” or “studying various types of” or “finding ways to provide more” reliable communication to connect the Internet to various services is in demand. In this paper, we analyze the security threats caused by malicious activities in the future Internet and propose a human behavior analysis-based security service model for malware detection and intrusion prevention to provide more reliable communication. Our proposed service model provides high reliability services by responding to security threats by detecting various malware intrusions and protocol authentications based on human behavior.

The Virtual Local Area Network (VLAN) has been used for a long time in campus and enterprise networks as the most popular network virtualization solution. Due to the benefits and advantages achieved by using VLAN, network operators and administrators have been using it for constructing their networks up until now and have even extended it to manage the networking in a cloud computing system. However, their configuration is a complex, tedious, time-consuming, and error-prone process. Since Software Defined Networking (SDN) features the centralized network management and network programmability, it is a promising solution for handling the aforementioned challenges in VLAN management. In this paper, we first introduce a new architecture for campus and enterprise networks by leveraging SDN and OpenFlow. Next, we have designed and implemented an application for easily managing and flexibly troubleshooting the VLANs in this architecture. This application supports both static VLAN and dynamic VLAN configurations. In addition, we discuss the hybrid-mode operation where the packet processing is involved by both the OpenFlow control plane and the traditional control plane. By deploying a real test-bed prototype, we illustrate how our system works and then evaluate the network latency in dynamic VLAN operation.

In the conventional clustering algorithms, an object could be assigned to only one group. However, this is sometimes not the case in reality, there are cases where the data do not belong to one group. As against, the fuzzy clustering takes into consideration the degree of fuzzy membership of each pixel relative to different classes. In order to overcome some shortcoming with traditional clustering methods, such as slow convergence and their sensitivity to initialization values, we have used the Harmony Search algorithm. It is based on the population metaheuristic algorithm, imitating the musical improvisation process. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. We propose in this paper a new unsupervised clustering method called the Fuzzy Harmony Search-Fourier Transform (FHS-FT). It is based on hybridization fuzzy clustering and the harmony search algorithm to increase its exploitation process and to further improve the generated solution, while the Fourier transform to increase the size of the image's data. The results show that the proposed method is able to provide viable solutions as compared to previous work

We studied the current state-of-the-art of Smart TV, the challenges and the drawbacks. Mainly we discussed the lack of end-to-end solution. We then illustrated the differences between Smart TV and IPTV from network service provider point of view. Unlike IPTV, viewer of Smart TV’s over-the-top (OTT) services could be global, such as foreign nationals in a country or viewers having special viewing preferences. Those viewers are sparsely distributed. The existing TV service deployment models over Internet are not suitable for such viewers as they are based on content popularity, hence we propose a community based service deployment methodology with proactive content caching on rendezvous points (RPs). In our proposal, RPs are intermediate nodes responsible for caching routing and decision making. The viewer’s community formation is based on geographical locations and similarity of their interests. The idea of using context information to do proactive caching is itself not new, but we combined this with “in network caching” mechanism of content centric network (CCN) architecture. We gauge the performance improvement achieved by a community model. The result shows that when the total numbers of requests are same; our model can have significantly better performance, especially for sparsely distributed communities

Our approach permits to capitalize the expert’s knowledge as business rules by using an agent-based platform. The objective of our approach is to allow experts to manage the daily evolutions of business domains without having to use a technician, and to allow them to be implied, and to participate in the development of the application to accomplish the daily tasks of their work. Therefore, the manipulation of an expert’s knowledge generates the need for information security and other associated technologies. The notion of cryptography has emerged as a basic concept in business rules modeling. The purpose of this paper is to present a cryptographic algorithm based approach to integrate the security aspect in business rules modeling. We propose integrating an agent-based approach in the framework. This solution utilizes a security agent with domain ontology. This agent applies an encryption/decryption algorithm to allow for the confidentiality, authenticity, and integrity of the most important rules. To increase the security of these rules, we used hybrid cryptography in order to take advantage of symmetric and asymmetric algorithms. We performed some experiments to find the best encryption algorithm, which provides improvement in terms of response time, space memory, and security

In watermarking schemes, the discrete wavelet transform (DWT) is broadly used because its frequency component separation is very useful. Moreover, LU decomposition has little influence on the visual quality of the watermark. Hence, in this paper, a novel blind watermark algorithm is presented based on LU transform and DWT for the copyright protection of digital images. In this algorithm, the color host image is first performed with DWT. Then, the horizontal and vertical diagonal high frequency components are extracted from the wavelet domain, and the sub-images are divided into 4×4 non-overlapping image blocks. Next, each sub-block is performed with LU decomposition. Finally, the color image watermark is transformed by Arnold permutation, and then it is inserted into the upper triangular matrix. The experimental results imply that this algorithm has good features of invisibility and it is robust against different attacks to a certain degree, such as contrast adjustment, JPEG compression, salt and pepper noise, cropping, and Gaussian noise

To resolve the problems of Poisson/impulse noise, blurriness, and sharpness in degraded X-ray images, a novel and efficient enhancement algorithm based on X-ray image fusion using a discrete wavelet transform is proposed in this paper. The proposed algorithm consists of two basics. First, it applies the techniques of boundary division to detect Poisson and impulse noise corrupted pixels and then uses the Wiener filter approach to restore those corrupted pixels. Second, it applies the sharpening technique to the same degraded X-ray image. Thus, it has two source X-ray images, which individually preserve the enhancement effects. The details and approximations of these sources X-ray images are fused via different fusion rules in the wavelet domain. The results of the experiment show that the proposed algorithm successfully combines the merits of the Wiener filter and sharpening and achieves a significant proficiency in the enhancement of degraded X-ray images exhibiting Poisson noise, blurriness, and edge details.

Along with the evolution of Internet and its new emerging services, the quantity and impact of attacks have been continuously increasing. Currently, the technical capability to attack has tended to decrease. On the contrary, performances of hacking tools are evolving, growing, simple, comprehensive, and accessible to the public. In this work, network penetration testing and auditing of the Redhat operating system (OS) are highlighted as one of the most popular OS for Internet applications. Some types of attacks are from a different side and new attack method have been attempted, such as: scanning for reconnaissance, guessing the password, gaining privileged access, and flooding the victim machine to decrease availability. Some analyses in network auditing and forensic from victim server are also presented in this paper. Our proposed system aims confirmed as hackable or not and we expect for it to be used as a reference for practitioners to protect their systems from cyber-attacks.

Smart grids propose new solutions for electricity consumers as a means to help them use energy in an efficient way. In this paper, we consider the demand-side management issue that exists for a group of consumers (houses) that are equipped with renewable energy (wind turbines) and storage units (battery), and we try to find the optimal scheduling for their home appliances, in order to reduce their electricity bills. Our simulation results prove the effectiveness of our approach, as they show a significant reduction in electricity costs when using renewable energy and battery storage.

Fuzzy Formal Concept Analysis (FCA) is a mathematical tool for the effective representation of imprecise and vague knowledge. However, with a large number of formal concepts from a fuzzy context, the task of knowledge representation becomes complex. Hence, knowledge reduction is an important issue in FCA with a fuzzy setting. The purpose of this current study is to address this issue by proposing a method that computes the corresponding crisp order for the fuzzy relation in a given fuzzy formal context. The obtained formal context using the proposed method provides a fewer number of concepts when compared to original fuzzy context. The resultant lattice structure is a reduced form of its corresponding fuzzy concept lattice and preserves the specialized and generalized concepts, as well as stability. This study also shows a step-by-step demonstration of the proposed method and its application.

In this paper, we propose a maximum entropy-based model, which can mathematically explain the bio- molecular event extraction problem. The proposed model generates an event table, which can represent the relationship between an event trigger and its arguments. The complex sentences with distinctive event structures can be also represented by the event table. Previous approaches intuitively designed a pipeline system, which sequentially performs trigger detection and arguments recognition, and thus, did not clearly explain the relationship between identified triggers and arguments. On the other hand, the proposed model generates an event table that can represent triggers, their arguments, and their relationships. The desired events can be easily extracted from the event table. Experimental results show that the proposed model can cover 91.36% of events in the training dataset and that it can achieve a 50.44% recall in the test dataset by using the event table.

The stream cipher Salsa20 and its reduced versions are among the fastest stream ciphers available today. However, Salsa20/7 is broken and Salsa20/12 is not as safe as before. Therefore, Salsa20 must completely perform all of the four rounds of encryption to achieve a good diffusion in order to resist the known attacks. In this paper, a new variant of Salsa20 that uses the chaos theory and that can achieve diffusion faster than the original Salsa20 is presented. The method has been tested and benchmarked with the original Salsa20 with a series of tests. Most of the tests show that the proposed chaotic Salsa of two rounds is faster than the original four rounds of Salsa20/4, but it offers the same diffusion level.

Recently, there has been an increasing demand of high data rates services, where several multiuser multiple- input multiple-output (MU-MIMO) techniques were introduced to meet these demands. Among these tech- niques, vector perturbation combined with linear precoding techniques, such as zero-forcing and minimum mean-square error, have been proven to be efficient in reducing the transmit power and hence, perform close to the optimum algorithm. In this paper, we review several fixed-complexity vector perturbation techniques and investigate their performance under both perfect and imperfect channel knowledge at the transmitter. Also, we investigate the combination of block diagonalization with vector perturbation outline its merits.

The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent’s position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.

Most traditional database systems exploit a record-oriented model where the attributes of a record are placed contiguously in a hard disk to achieve high performance writes. However, for read-mostly data warehouse systems, the column-oriented database has become a proper model because of its superior read performance. Today, flash memory is largely recognized as the preferred storage media for high-speed database systems. In this paper, we introduce a column-oriented database model based on flash memory and then propose a new column-aware flash indexing scheme for the high-speed column-oriented data warehouse systems. Our index management scheme, which uses an enhanced B+-Tree, achieves superior search performance by indexing an embedded segment and packing an unused space in internal and leaf nodes. Based on the performance results of two test databases, we concluded that the column-aware flash index management outperforms the traditional scheme in the respect of the mixed operation throughput and its response time.

We present a secure and robust image watermarking scheme that uses combined reversible DWT-DCT-SVD transformations to increase integrity, authentication, and confidentiality. The proposed scheme uses two different kinds of watermarking images: a reversible watermark, W1, which is used for verification (ensuring integrity and authentication aspects); and a second one, W2, which is defined by a logo image that provides confidentiality. Our proposed scheme is shown to be robust, while its performances are evaluated with respect to the peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), normalized cross-correlation (NCC), and running time. The robustness of the scheme is also evaluated against different attacks, including a compression attack and Salt & Pepper attack.

Recognition systems for scanned or printed music scores that have been implemented on personal computers have received attention from numerous scientists and have achieved significant results over many years. A modern trend with music scores being captured and played directly on mobile devices has become more interesting to researchers. The limitation of resources and the effects of illumination, distortion, and inclination on input images are still challenges to these recognition systems. In this paper, we introduce a novel approach for recognizing music scores captured by mobile cameras. To reduce the complexity, as well as the computational time of the system, we grouped all of the symbols extracted from music scores into ten main classes. We then applied each major class to SVM to classify the musical symbols separately. The experimental results showed that our proposed method could be applied to real time applications and that its performance is competitive with other methods.

IEEE 802.11p is a standard MAC protocol for wireless access in vehicular environments (WAVEs). If a packet collision happens when a safety message is sent out, IEEE 802.11p chooses a random back-off counter value in a fixed-size contention window. However, depending on the random choice of back-off counter value, it is still possible that less important messages are sent out first while more important messages are delayed longer until sent out. In this paper, we present a new scheme for safety message scheduling, called the enhanced message priority mechanism (EMPM). It consists of the following two components: the benefit-value algorithm, which calculates the priority of the messages depending on the speed, deceleration, and message lifetime; and the back-off counter selection algorithm, which chooses the non-uniform back-off counter value in order to reduce the collision probability and to enhance the throughput of the highly beneficial messages. Numerical results show that the EMPM can significantly improve the throughput and delay of messages with high benefits when compared with existing MAC protocols. Consequently, the EMPM can provide better QoS support for the more important and urgent messages.

Wireless Video Sensor Networks (WVSNs) have become a leading solution in many important applications, such as disaster recovery. By using WVSNs in disaster scenarios, the main goal is achieving a successful immediate response including search, location, and rescue operations. The achievement of such an objective in the presence of obstacles and the risk of sensor damage being caused by disasters is a challenging task. In this paper, we propose a fault tolerance model of WVSN for efficient post-disaster management in order to assist rescue and preparedness operations. To get an overview of the monitored area, we used video sensors with a rotation capability that enables them to switch to the best direction for getting better multimedia coverage of the disaster area, while minimizing the effect of occlusions. By constructing different cover sets based on the field of view redundancy, we can provide a robust fault tolerance to the network. We demonstrate by simulating the benefits of our proposal in terms of reliability and high coverage.

This paper presents the applications of spatial interpolation and assimilation methods for satellite and ground meteorological data, including temperature, relative humidity, and precipitation in regions of Vietnam. In this work, Universal Kriging is used for spatially interpolating ground data and its interpolated results are assimilated with corresponding satellite data to anticipate better gridded data. The input meteorological data was collected from 98 ground weather stations located all over Vietnam; whereas, the satellite data consists of the MODIS Atmospheric Profiles product (MOD07), the ASTER Global Digital Elevation Map (ASTER DEM), and the Tropical Rainfall Measuring Mission (TRMM) in six years. The outputs are gridded fields of temperature, relative humidity, and precipitation. The empirical results were evaluated by using the Root mean square error (RMSE) and the mean percent error (MPE), which illustrate that Universal Kriging interpolation obtains higher accuracy than other forms of Kriging; whereas, the assimilation for precipitation gradually reduces RMSE and significantly MPE. It also reveals that the accuracy of temperature and humidity when employing assimilation that is not significantly improved because of low MODIS retrieval due to cloud contamination.

Edit distance metrics are widely used for many applications such as string comparison and spelling error corrections. Hamming distance is a metric for two equal length strings and Damerau-Levenshtein distance is a well-known metrics for making spelling corrections through string-to-string comparison. Previous distance metrics seems to be appropriate for alphabetic languages like English and European languages. However, the conventional edit distance criterion is not the best method for agglutinative languages like Korean. The reason is that two or more letter units make a Korean character, which is called as a syllable. This mechanism of syllable-based word construction in the Korean language causes an edit distance calculation to be inefficient. As such, we have explored a new edit distance method by using consonant normalization and the normalization factor.

Generally, the wireless network provides priority to handover calls instead of new calls to maintain its quality of service (QoS). Because of this QoS provisioning, a call admission control (CAC) scheme is essential for the suitable management of limited radio resources of wireless networks to uphold different factors, such as new call blocking probability, handover call dropping probability, channel utilization, etc. Designing an optimal CAC scheme is still a challenging task due to having a number of considerable factors, such as new call blocking probability, handover call dropping probability, channel utilization, traffic rate, etc. Among existing CAC schemes such as, fixed guard band (FGB), fractional guard channel (FGC), limited fractional channel (LFC), and Uniform Fractional Channel (UFC), the LFC scheme is optimal considering the new call blocking and handover call dropping probability. However, this scheme does not consider channel utilization. In this paper, a CAC scheme, which is termed by a uniform fractional band (UFB) to overcome the limitations of existing schemes, is proposed. This scheme is oriented by priority and non-priority guard channels with a set of fractional channels instead of fractionizing the total channels like FGC and UFC schemes. These fractional channels in the UFB scheme accept new calls with a predefined uniform acceptance factor and assist the network in utilizing more channels. The mathematical models, operational benefits, and the limitations of existing CAC schemes are also discussed. Subsequently, we prepared a comparative study between the existing and proposed scheme in terms of the aforementioned QoS related factors. The numerical results we have obtained so far show that the proposed UFB scheme is an optimal CAC scheme in terms of QoS and resource utilization as compared to the existing schemes.

Region of interest (ROI) is the most informative part of a medical image and mostly has been used as a major part of watermark. Various shapes ROIs selection have been reported in region-based watermarking techniques. In region-based watermarking schemes an image region of non-interest (RONI) is the second important part of the image and is used mostly for watermark encapsulation. In online healthcare systems the ROI wrong selection by missing some important portions of the image to be part of ROI can create problem at the destination. This paper discusses the complete medical image availability in original at destination using the whole image as a watermark for authentication, tamper localization and lossless recovery (WITALLOR). The WITALLOR watermarking scheme ensures the complete image security without of ROI selection at the source point as compared to the other region-based watermarking techniques. The complete image is compressed using the Lempel-Ziv-Welch (LZW) lossless compression technique to get the watermark in reduced number of bits. Bits reduction occurs to a number that can be completely encapsulated into image. The watermark is randomly encapsulated at the least significant bits (LSBs) of the image without caring of the ROI and RONI to keep the image perceptual degradation negligible. After communication, the watermark is retrieved, decompressed and used for authentication of the whole image, tamper detection, localization and lossless recovery. WITALLOR scheme is capable of any number of tampers detection and recovery at any part of the image. The complete authentic image gives the opportunity to conduct an image based analysis of medical problem without restriction to a fixed ROI.

A mobile terminal will expect a number of handoffs within its call duration. In the event of a mobile call, when a mobile node moves from one cell to another, it should connect to another access point within its range. In case there is a lack of support of its own network, it must changeover to another base station. In the event of moving on to another network, quality of service parameters need to be considered. In our study we have used the Markov decision process approach for a seamless handoff as it gives the optimum results for selecting a network when compared to other multiple attribute decision making processes. We have used the network cost function for selecting the network for handoff and the connection reward function, which is based on the values of the quality of service parameters. We have also examined the constant bit rate and transmission control protocol packet delivery ratio. We used the policy iteration algorithm for determining the optimal policy. Our enhanced handoff algorithm outperforms other previous multiple attribute decision making methods.

The free distance of (n, k, l) convolutional codes has some connection with the memory length, which depends on not only l but also on k. To efficiently obtain a large memory length, we have constructed a new class of (2k, k, l) convolutional codes by (2k, k) block codes and (2, 1, l) convolutional codes, and its encoder and generation function are also given in this paper. With the help of some matrix modules, we designed a single structure Viterbi decoder with a parallel capability, obtained a unified and efficient decoding model for (2k, k, l) convolutional codes, and then give a description of the decoding process in detail. By observing the survivor path memory in a matrix viewer, and testing the role of the max module, we implemented a simulation with (2k, k, l) convolutional codes. The results show that many of them are better than conventional (2, 1, l) convolutional codes.

Over the last couple of years, traditional VANET (Vehicular Ad Hoc NETwork) evolved into VANET-based clouds. From the VANET standpoint, applications became richer by virtue of the boom in automotive telematics and infotainment technologies. Nevertheless, the research community and industries are concerned about the under-utilization of rich computation, communication, and storage resources in middle and high-end vehicles. This phenomenon became the driving force for the birth of VANET-based clouds. In this paper, we envision a novel application layer of VANET-based clouds based on the cooperation of the moving cars on the road, called CaaS (Cooperation as a Service). CaaS is divided into TIaaS (Traffic Information as a Service), WaaS (Warning as a Service), and IfaaS (Infotainment as a Service). Note, however, that this work focuses only on TIaaS and WaaS. TIaaS provides vehicular nodes, more precisely subscribers, with the fine-grained traffic information constructed by CDM (Cloud Decision Module) as a result of the cooperation of the vehicles on the roads in the form of mobility vectors. On the other hand, WaaS provides subscribers with potential warning messages in case of hazard situations on the road. Communication between the cloud infrastructure and the vehicles is done through GTs (Gateway Terminals), whereas GTs are physically realized through RSUs (Road-Side Units) and vehicles with 4G Internet access. These GTs forward the coarse-grained cooperation from vehicles to cloud and fine-grained traffic information and warnings from cloud to vehicles (subscribers) in a secure, privacy-aware fashion. In our proposed scheme, privacy is conditionally preserved wherein the location and the identity of the cooperators are preserved by leveraging the modified location-based encryption and, in case of any dispute, the node is subject to revocation. To the best of our knowledge, our proposed scheme is the first effort to offshore the extended traffic view construction function and warning messages dissemination function to the cloud.

New IIR digital differintegrators (differentiator and integrator) with very minor absolute relative errors are presented in this paper. The digital integrator is designed by interpolating some of the existing integrators. The optimum value of the interpolation ratio is obtained through linear programming optimization. Subsequently, by modifying the transfer function of the proposed integrator appropriately, new digital differentiator is obtained. Simulation results demonstrate that the proposed differintegrator are a more accurate approximation of ideal ones, than the existing differintegrators. Furthermore, the proposed differentiator has been tested in an image processing application. Edges characterize boundaries and are, therefore, a problem of fundamental importance in image processing. For comparison purpose Prewitt, Sobel, Roberts, Canny, Laplacian of Gaussian (LOG), Zerocross operators were used and their results are displayed. The results of edge detection by some of the existing differentiators are also provided. The simulation results have shown the superiority of the proposed approach over existing ones.

Skin detection is used in many applications, such as face recognition, hand
tracking, and human-computer interaction. There are many skin color detection
algorithms that are used to extract human skin color regions that are based on the thresholding technique since it is simple and fast for computation. The efficiency of each color space depends on its robustness to the change in lighting and the ability to distinguish skin color pixels in images that have a complex background. For more accurate skin detection, we are proposing a new threshold based on RGB and YUV color spaces. The proposed approach starts by converting the RGB color space to the YUV color model. Then it separates the Y channel, which represents the intensity of the color model from the U and V channels to eliminate the effects of luminance. After that the threshold values are selected based on the testing of the boundary of skin colors with the help of the color histogram. Finally, the threshold was applied to the input image to extract skin parts. The detected skin regions were quantitatively compared to the actual skin parts in the input images to measure the accuracy and to compare the results of our threshold to the results of other"'"s thresholds to prove the efficiency of our approach. The results of the experiment show that the proposed threshold is more robust in terms of dealing with the complex background and light conditions than others.

The accuracy of training-based activity recognition depends on the training procedure and the extent to which the training dataset comprehensively represents the activity and its varieties. Additionally, training incurs substantial cost and effort in the process of collecting training data. To address these limitations, we have developed a training-free activity recognition approach based on a fuzzy logic algorithm that utilizes a generic activity model and an associated activity semantic knowledge. The approach is validated through experimentation with real activity datasets. Results show that the fuzzy logic based algorithms exhibit comparable or better accuracy than other trainingbased approaches.

In the cursive handwriting recognition process, script trajectory segmentation and modeling represent an important task for large or open lexicon context that becomes more complicated in multi-writer applications. In this paper, we will present a developed system of Arabic online handwriting modeling based on graphemes segmentation and the extraction of its geometric features. The main contribution consists of adapting the Fourier descriptors to model the open trajectory of the segmented graphemes. To segment the trajectory of the handwriting, the system proceeds by first detecting its baseline by checking combined geometric and logic conditions. Then, the detected baseline is used as a topologic reference for the extraction of particular points that delimit the graphemes’ trajectories. Each segmented grapheme is then represented by a set of relevant geometric features that include the vector of the Fourier descriptors for trajectory shape modeling, normalized metric parameters that model the grapheme dimensions, its position in respect to the baseline, and codes for the description of its associated diacritics.

Biometric performance improvement is a challenging task. In this paper, a hierarchical strategy fusion based on multimodal biometric system is presented. This strategy relies on a combination of several biometric traits using a multi-level biometric fusion hierarchy. The multi-level biometric fusion includes a pre-classification fusion with optimal feature selection and a post-classification fusion that is based on the similarity of the maximum of matching scores. The proposed solution enhances biometric recognition performances based on suitable feature selection and reduction, such as principal component analysis (PCA) and linear discriminant analysis (LDA), as much as not all of the feature vectors components support the performance improvement degree.

The performance of edge detection often relies on its ability to correctly determine the dissimilarities of connected pixels. For grayscale images, the dissimilarity of two pixels is estimated by a scalar difference of their intensities and for color images, this is done by using the vector difference (color distance) of the three-color components. The Euclidean distance in the RGB color space typically measures a color distance. However, the RGB space is not suitable for edge detection since its color components do not coincide with the information human perception uses to separate objects from backgrounds. In this paper, we propose a novel method for color edge detection by taking advantage of the HSV color space and the Mahalanobis distance. The HSV space models colors in a manner similar to human perception. The Mahalanobis distance independently considers the hue, saturation, and lightness and gives them different degrees of contribution for the measurement of color distances. Therefore, our method is robust against the change of lightness as compared to previous approaches. Furthermore, we will introduce a noise-resistant technique for determining image gradients. Various experiments on simulated and real-world images show that our approach outperforms several existing methods, especially when the images vary in lightness or are corrupted by noise.

Nowadays mobile users are using a popular service called Location-Based Services (LBS). LBS is very helpful for a mobile user in finding various Point of Interests (POIs) in their vicinity. To get these services, users must provide their personal information, such as user identity or current location, which severely risks the location privacy of the user. Many researchers are developing schemes that enable a user to use these LBS services anonymously, but these approaches have some limitations (i.e., either the privacy prevention mechanism is weak or the cost of the solution is too much). As such, we are presenting a robust scheme for mobile users that allows them to use LBS anonymously. Our scheme involves a client side application that interacts with an untrusted LBS server to find the nearest POI for a service required by a user. The scheme is not only efficient in its approach, but is also very practical with respect to the computations that are done on a client’s resource constrained device. With our scheme, not only can a client anonymously use LBS without any use of a trusted third party, but also a server’s database is completely secure from the client. We performed experiments by developing and testing an Android-based client side smartphone application to support our argument.

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR.
The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document"'"s textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier.
In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.

Ranking thousands of web documents so that they are matched in response to a user query is really a challenging task. For this purpose, search engines use different ranking mechanisms on apparently related resultant web documents to decide the order in which documents should be displayed. Existing ranking mechanisms decide on the order of a web page based on the amount and popularity of the links pointed to and emerging from it. Sometime search engines result in placing less relevant documents in the top positions in response to a user query. There is a strong need to improve the ranking strategy. In this paper, a novel ranking mechanism is being proposed to rank the web documents that consider both the HTML structure of a page and the contextual senses of keywords that are present within it and its back-links. The approach has been tested on data sets of URLs and on their back-links in relation to different topics. The experimental result shows that the overall search results, in response to user queries, are improved. The ordering of the links that have been obtained is compared with the ordering that has been done by using the page rank score. The results obtained thereafter shows that the proposed mechanism contextually puts more related web pages in the top order, as compared to the page rank score.

A new secure network communication technique that has been designed for mobile wireless services, is presented in this paper. Its network services are mobile, distributed, seamless, and secure. We focus on the security of the scheme and achieve anonymity and reliability by using cryptographic techniques like blind signature and the electronic coin. The question we address in this paper is, “What is the best way to protect the privacy and anonymity of users of mobile wireless networks, especially in practical applications like e-commerce?” The new scheme is a flexible solution that answers this question. It efficiently protects user"'"s privacy and anonymity in mobile wireless networks and supports various applications. It is employed to implement a secure e-auction as an example, in order to show its advantages in practical network applications.

Recently, a pixel-chaotic-shuffling (PCS) method has been proposed by Huang et al. for encrypting color images using multiple chaotic systems like the Henon, the Lorenz, the Chua, and the Rossler systems. All of which have great encryption performance. The authors claimed that their pixel-chaotic-shuffle (PCS) encryption method has high confidential security. However, the security analysis of the PCS method against the chosen-plaintext attack (CPA) and known-plaintext attack (KPA) performed by Solak et al. successfully breaks the PCS encryption scheme without knowing the secret key. In this paper we present an improved shuffling pattern for the plaintext image bits to make the cryptosystem proposed by Huang et al. resistant to chosen-plaintext attack and known-plaintext attack. The modifications in the existing PCS encryption method are proposed to improve its security performance against the potential attacks described above. The Number of Pixel Change Rate (NPCR), Unified Average Changed Intensity (UACI), information entropy, and correlation coefficient analysis are performed to evaluate the statistical performance of the modified PCS method. The simulation analysis reveals that the modified PCS method has better statistical features and is more resistant to attacks than Huang et al.’s PCS method.

Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately 91.33 ± 0.67 % for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.

In order to classify a web page as being benign or malicious, we designed 14 basic and 16 extended features. The basic features that we implemented were selected to represent the essential characteristics of a web page. The system heuristically combines two basic features into one extended feature in order to effectively distinguish benign and malicious pages. The support vector machine can be trained to successfully classify pages by using these features. Because more and more malicious web pages are appearing, and they change so rapidly, classifiers that are trained by old data may misclassify some new pages. To overcome this problem, we selected an adaptive support vector machine (aSVM) as a classifier. The aSVM can learn training data and can quickly learn additional training data based on the support vectors it obtained during its previous learning session. Experimental results verified that the aSVM can classify malicious web pages adaptively.

This research paper proposes a secured, robust approach of information security using steganography. It presents two component based LSB (Least Significant Bit) steganography methods for embedding secret data in the least significant bits of blue components and partial green components of random pixel locations in the edges of images. An adaptive LSB based steganography is proposed for embedding data based on the data available in MSB’s (Most Significant Bits) of red, green, and blue components of randomly selected pixels across smooth areas. A hybrid feature detection filter is also proposed that performs better to predict edge areas even in noisy conditions. AES (Advanced Encryption Standard) and random pixel embedding is incorporated to provide two-tier security. The experimental results of the proposed approach are better in terms of PSNR and capacity. The comparison analysis of output results with other existing techniques is giving the proposed approach an edge over others. It has been thoroughly tested for various steganalysis attacks like visual analysis, histogram analysis, chi-square, and RS analysis and could sustain all these attacks very well.

Hardware-Software co-simulation of a multiple image encryption technique shall be described in this paper. Our proposed multiple image encryption technique is based on the Latin Square Image Cipher (LSIC). First, a carrier image that is based on the Latin Square is generated by using 256-bits of length key. The XOR operation is applied between an input image and the Latin Square Image to generate an encrypted image. Then, the XOR operation is applied between the encrypted image and the second input image to encrypt the second image. This process is continues until the nth input image is encrypted. We achieved hardware co-simulation of the proposed multiple image encryption technique by using the Xilinx System Generator (XSG). This encryption technique is modeled using Simulink and XSG Block set and synthesized onto Virtex 2 pro FPGA device. We validated our proposed technique by using the hardware software co-simulation method.

Numerous data intensive applications demand the efficient processing of a noew type of query, which is called a progressive query (PQ). A PQ consists of a set of unpredictable but inter-related step-queries (SQ) that are specified by its user in a sequence of steps. A conventional DBMS was not designed to efficiently process such PQs. In our earlier work, we introduced a materialized view based approach for efficiently processing PQs, where the focus was on selecting promising views for materialization. The problem of how to efficiently find usable views from the materialized set in order to answer the SQs for a PQ remains open. In this paper, we present a new index technique, called the Dynamic Materialized View Index(DMVI), to rapidly discover usable views for answering a given SQ. The structure of the proposed index is a special ordered tree where the SQ domain tables are used as search keys and some bitmaps are kept at the leaf nodes for refined filtering. A two-level priority rule is adopted to order domain tables in the tree, which facilitates the efficient maintenance of the tree by taking into account the dynamic characteristics of various types of materialized views for PQs. The bitmap encoding methods and the strategies/algorithms to construct, search, and maintain the DMVI are suggested. The extensive experimental results demonstrate that our index technique is quite promising in improving the performance of the materialized view based query processing approach for PQs

The most important things for a forest fire detection system are the exact extraction of the smoke from image and being able to clearly distinguish the smoke from those with similar qualities, such as clouds and fog. This research presents an intelligent forest fire detection algorithm via image processing by using the Gaussian Mixture model (GMM), which can be applied to detect smoke at the earliest time possible in a forest. GMMs are usually addressed by making the model adaptive so that its parameters can track changing illuminations and by making the model more complex so that it can represent multimodal backgrounds more accurately for smoke plume segmentation in the forest. Also, in this paper, we suggest a way to classify the smoke plumes via a feature extraction using HSL(Hue, Saturation and Lightness or Luminanace) color space analysis.

As we know every software development process is pretty large and consists of different modules. This raises the idea of prioritizing different software modules so that important modules can be tested by preference. In the software testing process, it is not possible to test each and every module regressively, which is due to time and cost constraints. To deal with these constraints, this paper proposes an approach that is based on the fuzzy multi-criteria approach for prioritizing several software modules and calculates optimal time and cost for software testing by using fuzzy logic and the fault tolerance approach.

This paper presents a dual modeling method that simulates the graphic and haptic behavior of a volumetric deformable object and conveys the behavior to a human operator. Although conventional modeling methods (a mass-spring model and a finite element method) are suitable for the real-time computation of an object"'"s deformation, it is not easy to compute the haptic behavior of a volumetric deformable object with the conventional modeling method in real-time (within a 1kHz) due to a computational burden. Previously, we proposed a fast volume haptic rendering method based on the S-chain model that can compute the deformation of a volumetric non-rigid object and its haptic feedback in real-time. When the S-chain model represents the object, the haptic feeling is realistic, whereas the graphical results of the deformed shape look linear. In order to improve the graphic and haptic behavior at the same time, we propose a dual modeling framework in which a volumetric haptic model and a surface graphical model coexist. In order to inspect the graphic and haptic behavior of objects represented by the proposed dual model, experiments are conducted with volumetric objects consisting of about 20,000 nodes at a haptic update rate of 1000Hz and a graphic update rate of 30Hz. We also conduct human factor studies to show that the haptic and graphic behavior from our model is realistic. Our experiments verify that our model provides a realistic haptic and graphic feeling to users in real-time.

This paper presents a study on a high-performance design for a block cipher algorithm implemented on modern many-core graphics processing units (GPUs). The recent emergence of VLSI technology makes it feasible to fabricate multiple processing cores on a single chip and enables general-purpose computation on a GPU (GPGPU). The GPU strategy offers significant performance improvements for all-purpose computation and can be used to support a broad variety of applications, including cryptography. We have proposed an efficient implementation of the encryption/decryption operations of a block cipher algorithm, SEED, on off-the-shelf NVIDIA many-core graphics processors. In a thorough experiment, we achieved high performance that is capable of supporting a high network speed of up to 9.5 Gbps on an NVIDIA GTX285 system (which has 240 processing cores). Our implementation provides up to 4.75 times higher performance in terms of encoding and decoding throughput as compared to the Intel 8-core system.

Shuffling is an effective method to build a publicly verifiable mix network to implement verifiable anonymous channels that can be used for important cryptographic applications like electronic voting and electronic cash. One shuffling scheme by Groth is claimed to be secure and efficient. However, its soundness has not been formally proven. An attack against the soundness of this shuffling scheme is presented in this paper. Such an attack compromises the soundness of the mix network based on it. Two new shuffling protocols are designed on the basis of Groth"'"s shuffling and batch verification techniques. The first new protocol is not completely sound, but is formally analyzed in regards to soundness, so it can be applied to build a mix network with formally proven soundness. The second new protocol is completely sound, so is more convenient to apply. Formal analysis in this paper guarantees that both new shuffling protocols can be employed to build mix networks with formally provable soundness. Both protocols prevent the attack against soundness in Groth"'"s scheme. Both new shuffling protocols are very efficient as batch-verification-based efficiency-improving mechanisms have been adopted. The second protocol is even simpler and more elegant than the first one as it is based on a novel batch cryptographic technique.

Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary; estimating a user"'"s interests typically involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked imposes stringent constraints on the "unlearning” capabilities of the estimator used. Therefore, resorting to strong estimators that converge with a probability of 1 is inefficient since they rely on the assumption that the distribution of the user"'"s preferences is stationary. In this vein, we propose to use a family of stochastic-learning based Weak estimators for learning and tracking a user"'"s time varying interests. Experimental results demonstrate that our proposed paradigm outperforms some of the traditional legacy approaches that represent the state-of-the-art technology.

The data grid provides geographically distributed resources for large-scale applications. It generates a large set of data. The replication of this data in several sites of the grid is an effective solution for achieving good performance. In this paper we propose an approach of dynamic replication in a hierarchical grid that takes into account crash failures in the system. The replication decision is taken based on two parameters: the availability and popularity of the data. The administrator requires a minimum rate of availability for each piece of data according to its access history in previous periods, but this availability may increase if the demand is high on this data. We also proposed a strategy to keep the desired availability respected even in case of a failure or rarity (nopopularity) of the data. The simulation results show the effectiveness of our replication strategy in terms of response time, the unavailability of requests, and availability

Task-based programming is becoming the state-of-the-art method of choice for extracting the desired performance from multi-core chips. It expresses a program in terms of lightweight logical tasks rather than heavyweight threads. Intel Threading Building Blocks (TBB) is a task-based parallel programming paradigm for multi-core processors. The performance gain of this paradigm depends to a great extent on the efficiency of its parallel constructs. The parallel overheads incurred by parallel constructs determine the ability for creating large-scale parallel programs, especially in the case of fine-grain parallelism. This paper presents a study of TBB parallelization overheads. For this purpose, a TBB micro-benchmarks suite called TBBench has been developed. We use TBBench to evaluate the parallelization overheads of TBB on different multi-core machines and different compilers. We report in detail in this paper on the relative overheads and analyze the running results.

Recently, security researches have been processed on the method to cover a broader range of hacking attacks at the low level in the perspective of hardware. This system security applies not only to individuals' computer systems but also to cloud environments. "Cloud" concerns operations on the web. Therefore it is exposed to a lot of risks and the security of its spaces where data is stored is vulnerable. Accordingly, in order to reduce threat factors to security, the TCG proposed a highly reliable platform based on a semiconductor-chip, the TPM. However, there have been no technologies up to date that enables a real-time visual monitoring of the security status of a PC that is operated based on the TPM. And the TPB has provided the function in a visual method to monitor system status and resources only for the system behavior of a single host. Therefore, this paper will propose a m-TMS (Mobile Trusted Monitoring System) that monitors the trusted state of a computing environment in which a TPM chip-based TPB is mounted and the current status of its system resources in a mobile device environment resulting from the development of network service technology. The m-TMS is provided to users so that system resources of CPU, RAM, and process, which are the monitoring objects in a computer system, may be monitored. Moreover, converting and detouring single entities like a PC or target addresses, which are attack pattern methods that pose a threat to the computer system security, are combined. The branch instruction trace function is monitored using a BiT Profiling tool through which processes attacked or those suspected of being attacked may be traced, thereby enabling users to actively respond

In this paper, we propose a new machine vision algorithm for automatic defect detection on patterned textures with the help of texture-periodicity and the Jensen- Shannon Divergence, which is a symmetrized and smoothed version of the Kullback- Leibler Divergence. Input defective images are split into several blocks of the same size as the size of the periodic unit of the image. Based on histograms of the periodic blocks, Jensen-Shannon Divergence measures are calculated for each periodic block with respect to itself and all other periodic blocks and a dissimilarity matrix is obtained. This dissimilarity matrix is utilized to get a matrix of true-metrics, which is later subjected to Ward"'"s hierarchical clustering to automatically identify defective and defect-free blocks. Results from experiments on real fabric images belonging to 3 major wallpaper groups, namely, pmm, p2, and p4m with defects, show that the proposed method is robust in finding fabric defects with a very high success rates without any human intervention

Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

We propose an analytic model to compute the station’s saturated throughput and packet delay performance of the IEEE 802.11 DCF (Distributed Coordination Function) in which frame transmission error rates in the channel are different from each other. Our analytic model shows that a station experiencing worse frame error rates than the others suffers severe performance degradation below its deserved throughput and delay performance. 802.11 DCF adopts an exponential back-off scheme. When some stations suffer from high frame error rates, their back-off stages should be increased so that others get the benefit from the smaller collision probabilities. This impact is then recursively applied to degrade the performance of the victim stations. In particular, we show that the performance is considerably degraded even if the frame error rate of the victim station satisfies the receiver input level sensitivity that has been specified in the IEEE 802.11 standard. We also verify the analytic results by the OPNET simulations.

Despite the fact that the deployment of sensor networks and target tracking could both be managed by taking full advantage of Voronoi diagrams, very little few have been made in this regard. In this paper, we designed an optimized barrier coverage and an energy-efficient clustering algorithm for forming Vonoroi-based Wireless Sensor Networks(WSN) in which we proposed a mobile target tracking scheme (CTT&MAV) that takes full advantage of Voronoi-diagram boundary to improve detectability. Simulations verified that CTT&MAV outperforms random walk, random waypoint, random direction and Gauss-Markov in terms of both the average hop distance that the mobile target moved before being detected and lower sensor death rate. Moreover, we demonstrate that our results are robust as realistic sensing models and also validate our observations through extensive simulations.

Whether it is crosstalk, harmonics, or in-band operation of wireless technologies, interference between a reference system and a host of offenders is virtually unavoidable. In past contributions, a benchmark has been established and considered for coexistence analysis with a number of technologies including FWA, UMTS, and WiMAX. However, the previously presented model does not take into account the mobility factor of the reference node in addition to a number of interdependent requirements regarding the link direction, channel state, data rate and system factors; hence limiting its applicability for the MBWA (IEEE 802.20) standard. Thus, over diverse modes, in this correspondence we analytically derived the greatest aggregate interference level tolerated for high-fidelity transmission tailored specifically for the MBWA standard. Our results, in the form of benchmark indicators, should be of particular interest to peers analyzing and researching RF coexistence scenarios with this new protocol.

As a powerful and flexible processor, the Graphic Processing Unit (GPU) can offer a great faculty in solving many high-performance computing applications. Sweep3D, which simulates a single group time-independent discrete ordinates (Sn) neutron transport deterministically on 3D Cartesian geometry space, represents the key part of a real ASCI application. The wavefront process for parallel computation in Sweep3D limits the concurrent threads on the GPU. In this paper, we present multi-dimensional optimization methods for Sweep3D, which can be efficiently implemented on the finegrained parallel architecture of the GPU. Our results show that the overall performance of Sweep3D on the CPU-GPU hybrid platform can be improved up to 4.38 times as compared to the CPU-based implementation.

The enhanced pyramid graph was recently proposed as an interconnection network model in parallel processing for maximizing regularity in pyramid networks. We prove that there are two edge-disjoint Hamiltonian cycles in the enhanced pyramid networks. This investigation demonstrates its superior property in edge fault tolerance. This result is optimal in the sense that the minimum degree of the graph is only four.

In deeply scaled CMOS technologies, two major non-ideal factors are threatening the survival of the CMOS; i) PVT (process, voltage, and temperature) variations and ii) leakage power consumption. In this paper, we propose a novel postsilicon tuning methodology to scale optimum voltage and frequency ¡°dynamically¡±. The proposed design technique will use our PVT sensor circuits to monitor the variations and based on the monitored variation data, voltage and frequency will be compensated ¡°automatically¡±. During the compensation process, supply voltage is dynamically adjusted to guarantee the minimum total power consumption without violating the frequency requirement. The simulation results show that the proposed technique can reduce the total power by 85% and the static power by 53% on average for the selected ISCAS¡¯85 benchmark circuits with 45 nm CMOS technology compared to the results of the traditional PVT compensation method.

The concept of Smart-Homes is becoming more and more popular. It is anticipated that Radio Frequency IDentification (RFID) technology will play a major role in such environments. We can find many previously proposed schemes that focus solely on: authentication between the RFID tags and readers, and user privacy protection from malicious readers. There has also been much talk of a very popular RFID application: a refrigerator/bookshelf that can scan and list out the details of its items on its display screen. Realizing such an application is not as straight forward as it seems to be, especially in securely deploying such RFID-based applications in a smart home environment. Therefore this paper describes some of the RFID-based applications that are applicable to smart home environments. We then identify their related privacy and security threats and security requirements and also propose a secure approach, where RFID-tagged consumer items, RFID-reader enabled appliances (e.g., refrigerators), and RFID-based applications would securely interact among one another. At the moment our approach is just a conceptual idea, but it sheds light on very important security issues related to RFID-based applications that are beneficial for consumers.

Bivium is a simplified version of Trivium, a hardware profile finalist of the eSTREAM project. Bivium has an internal state size of 177 bits and a key length of 80 bits. In this paper, a guess and determine attack on this cipher is introduced. In the proposed method, the best linear approximations for the updating functions are first defined. Then by using these calculated approximations, a system of linear equations is built. By guessing 30 bits of internal state, the system is solved and all the other 147 remaining bits are determined. The complexity of the attack is O (230), which is an improvement to the previous guess and determine attack with a complexity of order O(252.3).

User authentication refers to user identification based on something a user knows, something a user has, something a user is or something the user does; it can also take place based on a combination of two or more of such factors. With the increasingly diverse risks in online environments, user authentication methods are also becoming more diversified. This research analyzes user authentication methods being used in various online environments, such as web portals, electronic transactions, financial services and e-government, to identify the characteristics and issues of such authentication methods in order to present a user authentication level system model suitable for different online services. The results of our method are confirmed through a risk assessment and we verify its safety using the testing method presented in OWASP and NIST SP800-63.

Vehicular networks are a promising application of mobile ad hoc networks. In this paper, we introduce an efficient broadcast technique, called CB-S (Cell Broadcast for Streets), for vehicular networks with occlusions such as skyscrapers. In this environment, the road network is fragmented into cells such that nodes in a cell can communicate with any node within a two cell distance. Each mobile node is equipped with a GPS (Global Positioning System) unit and a map of the cells. The cell map has information about the cells including their identifier and the coordinates of the upper-right and lower-left corner of each cell. CB-S has the following desirable property. Broadcast of a message is performed by rebroadcasting the message from every other cell in the terrain. This characteristic allows CB-S to achieve an efficient performance. Our simulation results indicate that messages always reach all nodes in the wireless network. This perfect coverage is achieved with minimal overhead. That is, CB-S uses a low number of nodes to disseminate the data packets as quickly as probabilistically possible. This efficiency gives it the advantage of low delay. To show these benefits, we give simulations results to compare CB-S with four other broadcast techniques. In practice, CB-S can be used for information dissemination, or to reduce the high cost of destination discovery in routing protocols. By also specify the radius of affected zone, CB-S is also more efficient when broadcast to a subset of the nodes is desirable.

The overhead of processing fine-grain tasks on a grid induces the need for batch processing or task group deployment in order to minimise overall application turnaround time. When deciding the granularity of a batch, the processing requirements of each task should be considered as well as the utilisation constraints of the interconnecting network and the designated resources. However, the dynamic nature of a grid requires the batch size to be adaptable to the latest grid status. In this paper, we describe the policies and the specific techniques involved in the batch resizing process. We explain the nuts and bolts of these techniques in order to maximise the resulting benefits of batch processing. We conduct experiments to determine the nature of the policies and techniques in response to a real grid environment. The techniques are further investigated to highlight the important parameters for obtaining the appropriate task granularity for a grid resource.

A variety of different metrics has been introduced to measure the similarity of two given sequences. These widely used metrics are ranging from spell correctors and categorizers to new sequence mining applications. Different metrics consider different aspects of sequences, but the essence of any sequence is extracted from the ordering of its elements. In this paper, we propose a novel sequence similarity measure that is based on all ordered pairs of one sequence and where a Hasse diagram is built in the other sequence. In contrast with existing approaches, the idea behind the proposed sequence similarity metric is to extract all ordering features to capture sequence properties. We designed a clustering problem to evaluate our sequence similarity metric. Experimental results showed the superiority of our proposed sequence similarity metric in maximizing the purity of clustering compared to metrics such as d2, Smith-Waterman, Levenshtein, and Needleman-Wunsch. The limitation of those methods originates from some neglected sequence features, which are considered in our proposed sequence similarity metric.

The success of iris recognition depends mainly on two factors: image acquisition and an iris recognition algorithm. In this study, we present a system that considers both factors and focuses on the latter. The proposed algorithm aims to find out the most efficient wavelet family and its coefficients for encoding the iris template of the experiment samples. The algorithm implemented in software performs segmentation, normalization, feature encoding, data storage, and matching. By using the Haar and Biorthogonal wavelet families at various levels feature encoding is performed by decomposing the normalized iris image. The vertical coefficient is encoded into the iris template and is stored in the database. The performance of the system is evaluated by using the number of degrees of freedom, False Reject Rate (FRR), False Accept Rate (FAR), and Equal Error Rate (EER) and the metrics show that the proposed algorithm can be employed for an iris recognition system.

Microprocessors are becoming increasingly vulnerable to soft errors due to the current trends of semiconductor technology scaling. Traditional redundant multithreading architectures provide perfect fault tolerance by re-executing all the computations. However, such a full re-execution technique significantly increases the verification workload on the processor resources, resulting in severe performance degradation. This paper presents a pro-active verification management approach to mitigate the verification workload to increase its performance with a minimal effect on overall reliability. An anomaly-speculation-based filter checker is proposed to guide a verification priority before the re-execution process starts. This technique is accomplished by exploiting a value similarity property, which is defined by a frequent occurrence of partially identical values. Based on the biased distribution of similarity distance measure, this paper investigates further application to exploit similar values for soft error tolerance with anomaly speculation. Extensive measurements prove that the majority of instructions produce values, which are different from the previous result value, only in a few bits. Experimental results show that the proposed scheme accelerates the processor to be 180% faster than traditional fully-fault-tolerant processor with a minimal impact on overall soft error rate.

In this paper, we propose a new congestion control scheme for high-speed networks. The basic idea of our proposed scheme is to adopt a game theory called, “Minority Game” (MG), to realize a selective reduction of the transmission speed of senders. More concretely, upon detecting any congestion, the scheme starts a game among all senders who are participating in the communication. The losers of the game reduce the transmission speed by a multiplicative factor. MG is a game that has recently attracted considerable attention, and it is known to have a remarkable property so that the number of winners converges to a half the number of players in spite of the selfish behavior of the players to increase its own profit. By using this property of MG, we can realize a fair reduction of the transmission speed, which is more efficient than the previous schemes in which all senders uniformly reduce their transmission speed. The effect of the proposed scheme is evaluated by simulation. The result of simulations indicates that the proposed scheme certainly realizes a selective reduction of the transmission speed. It is sufficiently fair compared to other simple randomized schemes and is sufficiently efficient compared to other conventional schemes.

In ubiquitous environments, many applications need to process data with time and space dimensions. Because of this, there is growing attention not only on gathering spatiotemporal data in ubiquitous environments, but also on processing such data in databases. In order to obtain the full benefits from spatiotemporal data, we need a data model that naturally expresses the properties of spatiotemporal data. In this paper, we introduce three spatiotemporal data models extended from temporal data models. The main goal of this paper is to determine which data model is less complex in the spatiotemporal context. To this end, we compare their query languages in the complexity aspect because the complexity of a query language is tightly coupled with its underlying data model. Throughout our investigations, we show that it is important to intertwine space and time dimensions and keep one-to-one correspondence between an object in the real world and a tuple in a database in order to naturally express queries in ubiquitous applications.

Since security and privacy problems in RFID systems have attracted much attention, numerous RFID authentication protocols have been suggested. One of the various design approaches is to use light-weight logics such as bitwise Boolean operations and addition modulo 2m between m-bits words. Because these operations can be implemented in a small chip area, that is the major requirement in RFID protocols, a series of protocols have been suggested conforming to this approach. In this paper, we present new attacks on these lightweight RFID authentication protocols by using the Grobner basis. Our attacks are superior to previous ones for the following reasons: since we do not use the specific characteristics of target protocols, they are generally applicable to various ones. Furthermore, they are so powerful that we can recover almost all secret information of the protocols. For concrete examples, we show that almost all secret variables of six RFID protocols, LMAP, M2AP, EMAP, SASI, Lo et al."s protocol, and Lee et al."s protocol, can be recovered within a few seconds on a single PC.

It is important that desktop grids should be able to aggressively deal with the dynamic properties that arise from the volatility and heterogeneity of resources. Therefore, it is required that task scheduling be able to positively consider the execution behavior that is characterized by an individual resource. In this paper, we implement a log analysis system with REST web services, which can analyze the execution behavior by utilizing the actual log data of desktop grid systems. To verify the log analysis system, we conducted simulations and showed that the resource group-based task scheduling, based on the analysis of the execution behavior, offers a faster turnaround time than the existing one even if few resources are used.

Due to the convergence of voice, data, and video, today’s telecom operators are facing the complexity of service and network management to offer differentiated value-added services that meet customer expectations. Without the operations support of well-developed Business Support System/Operations Support System (BSS/OSS), it is difficult to timely and effectively provide competitive services upon customer request. In this paper, a suite of NGOSS-based Telecom OSS (TOSS) is developed for the support of fulfillment and assurance operations of telecom services and IT services. Four OSS groups, TOSS-P (intelligent service provisioning), TOSS-N (integrated large-scale network management), TOSS-T (trouble handling and resolution), and TOSS-Q (end-to-end service quality management), are organized and integrated following the standard telecom operation processes (i.e., eTOM). We use IPTV and IP-VPN operation scenarios to show how these OSS groups co-work to support daily business operations with the benefits of cost reduction and revenue acceleration.

GIS can only be applied to certain areas by storing format. It is subordinate to a system when displaying geographic information data. It is therefore inevitable for GIS to use GML that supports efficient usage of various geographic information data and interoperability for integration and sharing. The paper constructs VisualGML that translates currently-used geographic information such as DXF (Drawing Exchange Format), DWG (DraWinG), or SHP (Shapefile) into GML format for visualization. VisualGML constructs an integrated map pre-process module, which filters geographic information data according to its tag and properties, to provide the flexibility of a mobile device. VisualGML also provides two major GIS services for the user and administrator. It can enable visualizing location search. This is applied with a 3-Layer POI structure for the user. It has trace monitoring visualization through moving information of mobile devices for the administrator.

?In the secure communication areas, three-party authenticated key exchange protocol is an important cryptographic technique. In this protocol, two clients will share a human-memorable password with a trusted server, in which two users can generate a secure session key. On the other hand the protocol should resist all types of password guessing attacks. Recently, STPKE’ protocol has been proposed by Kim and Choi. An undetectable online password guessing attack on STPKE’ protocol is presented in the current study. An alternative protocol to overcome undetectable online password guessing attacks is proposed. The results show that the proposed protocol can resist undetectable online password guessing attacks. Additionally, it achieves the same security level with reduced random numbers and without XOR operations. The computational efficiency is improved by ? 30% for problems of size ? 2048 bits. The proposed protocol is achieving better performance efficiency and withstands password guessing attacks. The results show that the proposed protocol is secure, efficient and practical.

The rapid growth of communication and globalization has changed the software engineering process. Security has become a crucial component of any software system. However, software developers often lack the knowledge and skills needed to develop secure software. Clearly, the creation of secure software requires more than simply mandating the use of a secure software development lifecycle; the components produced by each stage of the lifecycle must be correctly implemented for the resulting system to achieve its intended goals. This study demonstrates that a more effective approach to the development of secure software can result from the integration of carefully selected security patterns into appropriate stages of the software development lifecycle to ensure that security designs are correctly implemented. The goal of this study is to provide developers with an Integrated Security Development Framework (ISDF) that can assist them in building more secure software.

The trend of Next Generation Networks’ (NGN) evolution is towards providing multiple and multimedia services to users through ubiquitous networks. The aim of IP Multimedia Subsystem (IMS) is to integrate mobile communication networks and computer networks. The IMS plays an important role in NGN services, which can be achieved by heterogeneous networks and different access technologies. IMS can be used to manage all service related issues such as Quality of Service (QoS), Charging, Access Control, User and Services Management. Nowadays, internet technology is changing with each passing day. New technologies yield new impact to IMS. In this paper, we perform a survey of IMS and discuss the different impacts of new technologies on IMS such as P2P, SCIM, Web Service and its security issues.

In this paper, we present a fine-grained localization algorithm for wireless sensor networks using a mobile beacon node. The algorithm is based on distance measurement using RSSI. The beacon node is equipped with a GPS sender and RF (radio frequency) transmitter. Each stationary sensor node is equipped with a RF. The beacon node periodically broadcasts its location information, and stationary sensor nodes perceive their positions as beacon points. A sensor node’s location is computed by measuring the distance to the beacon point using RSSI. Our proposed localization scheme is evaluated using OPNET 8.1 and compared with Ssu’s and Yu’s localization schemes. The results show that our localization scheme outperforms the other two schemes in terms of energy efficiency (overhead) and accuracy.

Nowadays due to the rapid advances in the field of information systems, transactional databases are being updated regularly and/or periodically. The knowledge discovered from these databases has to be maintained, and an incremental updating technique needs to be developed for maintaining the discovered association rules from these databases. The concept of Temporal Association Rules has been introduced to solve the problem of handling time series by including time expressions into association rules. In this paper we introduce a novel algorithm for Incremental Mining of General Temporal Association Rules (IMTAR) using an extended TFP-tree. The main benefits introduced by our algorithm are that it offers significant advantages in terms of storage and running time and it can handle the problem of mining general temporal association rules in incremental databases by building TFP-trees incrementally. It can be utilized and applied to real life application domains. We demonstrate our algorithm and its advantages in this paper.

This paper proposes a personal information protection model that allows a user to regulate his or her own personal information and privacy protection policies to receive services provided by a service provider without having to reveal personal information in a way that the user is opposed to. When the user needs to receive a service that requires personal information, the user will only reveal personal information that they find acceptable and for uses that they agree with. Users receive desired services from the service provider only when there is agreement between the user’s and the service provider’s security policies. Moreover, the proposed model utilizes a mobile agent that is transmitted from the user’s personal space, providing the user with complete control over their privacy protection. In addition, the mobile agent is itself a selfdestructing program that eliminates the possibility of personal information being leaked. The mobile agent described in this paper allows users to truly control access to their personal information.

This paper presents a measurement system for 3D hand-drawn gesture motion. Many pen-type input devices with Inertial Measurement Units (IMU) have been developed to estimate 3D hand-drawn gesture using the measured acceleration and/or the angular velocity of the device. The crucial procedure in developing these devices is to measure and to analyze their motion or trajectory. In order to verify the trajectory estimated by an IMU-based input device, it is necessary to compare the estimated trajectory to the real trajectory. For measuring the real trajectory of the pen-type device, a PHANToMTM haptic device is utilized because it allows us to measure the 3D motion of the object in real-time. Even though the PHANToMTM measures the position of the hand gesture well, poor initialization may produce a large amount of error. Therefore, this paper proposes a calibration method which can minimize measurement errors.

Most of the data warehouse (DW) requirements engineering approaches have not distinguished the early requirements engineering phase from the late requirements engineering phase. There are very few approaches seen in the literature that explicitly model the early & late requirements for a DW. In this paper, we propose an AGDI (Agent-Goal-Decision-Information) model to support the early and late requirements for the development of DWs. Here, the notion of agent refers to the stakeholders of the organization and the dependency among agents refers to the dependencies among stakeholders for fulfilling their organizational goals. The proposed AGDI model also supports three interrelated modeling activities namely, organization modeling, decision modeling and information modeling. Here, early requirements are modeled by performing organization modeling and decision modeling activities, whereas late requirements are modeled by performing information modeling activities. The proposed approach has been illustrated to capture the early and late requirements for the development of a university data warehouse exemplifying our model’s ability of supporting its decisional goals by providing decisional information.

Multi-Relay Networks should accommodate mobile users of various speeds. The cellular system should meet the minimum residency time requirements for handover calls while considering an efficient use of available channels. In this paper, we design speed-sensitive handover under dynamic hierarchical cellular systems, in which mobile users are classified according to the mean speed of mobile users and each class has its cellular layer. In order to meet the minimum residency time, the cell size of each cellular layer is dynamically determined depending on the distributions of mean speeds of mobile users. Since the speed-dependent non-preferred cell can provide a secondary resource, overflow and take-back schemes are adopted in the system. We develop analytical models to study the performance of the proposed system, and show that the optimal cell size improves the blocking probability.

This paper proposes a novel reversible data hiding scheme based on a Vector Quantization (VQ) codebook. The proposed scheme uses the principle component analysis (PCA) algorithm to sort the codebook and to find two similar codewords of an image block. According to the secret to be embedded and the difference between those two similar codewords, the original image block is transformed into a difference number table. Finally, this table is compressed by entropy coding and sent to the receiver. The experimental results demonstrate that the proposed scheme can achieve greater hiding capacity, about five bits per index, with an acceptable bit rate. At the receiver end, after the compressed code has been decoded, the image can be recovered to a VQ compressed image.

Cryptographic hash functions reduce inputs of arbitrary or very large length to a short string of fixed length. All hash function designs start from a compression function with fixed length inputs. The compression function itself is designed from scratch, or derived from a block cipher or a permutation. The most common procedure to extend the domain of a compression function in order to obtain a hash function is a simple linear iteration; however, some variants use multiple iterations or a tree structure that allows for parallelism. This paper presents a survey of 17 extenders in the literature. It considers the natural question whether these preserve the security properties of the compression function, and more in particular collision resistance, second preimage resistance, preimage resistance and the pseudo-random oracle property.

The Internet explosion and the increase in crucial web applications such as ebanking and e-commerce, make essential the need for network security tools. One of such tools is an Intrusion detection system which can be classified based on detection approachs as being signature-based or anomaly-based. Even though intrusion detection systems are well defined, their cooperation with each other to detect attacks needs to be addressed. Consequently, a new architecture that allows them to cooperate in detecting attacks is proposed. The architecture uses Software Agents to provide scalability and distributability. It works in two modes: learning and detection. During learning mode, it generates a profile for each individual system using a fuzzy data mining algorithm. During detection mode, each system uses the FuzzyJess to match network traffic against its profile. The architecture was tested against a standard data set produced by MIT Lincoln Laboratory and the primary results show its efficiency and capability to detect attacks. Finally, two new methods, the memory-window and memoryless-window, were developed for extracting useful parameters from raw packets. The parameters are used as detection metrics

The core services in cloud computing environment are SaaS (Software as a Service), Paas (Platform as a Service) and IaaS (Infrastructure as a Service). Among these three core services server virtualization belongs to IaaS and is a service technology to reduce the server maintenance expenses. Normally, the primary purpose of sever virtualization is building and maintaining a new well functioning server rather than using several existing servers, and in improving the various system performances. Often times this presents an issue in that there might be a need to increase expenses in order to build a new server. This study intends to use grid service architecture for a form of server virtualization which utilizes the existing servers rather than introducing a new server. More specifically, the proposed system is to enhance system performance and to reduce the corresponding expenses, by adopting a scheduling algorithm among the distributed servers and the constituents for grid computing thereby supporting the server virtualization service. Furthermore, the proposed server virtualization system will minimize power management by adopting the sleep severs, the subsidized servers and the grid infrastructure. The power maintenance expenses for the sleep servers will be lowered by utilizing the ACPI (Advanced Configuration & Power Interface) standards with the purpose of overcoming the limits of server performance.

As well as providing various APIs for the development of inference engines and storage models, Jena is widely used in the development of systems or tools related with Web ontology management. However, Jena still has several problems with regard to the development of real applications, one of the most important being that its query processing performance is unacceptable. This paper proposes a storage model to improve the query processing performance of the original Jena storage. The proposed storage model semantically classifies OWL elements, and stores an ontology in separately classified tables according to the classification. In particular, the hierarchical knowledge is managed, which can make the processing performance of inferable queries enhanced and stores information. It enhances the query processing performance by using hierarchical knowledge. For this paper an experimental evaluation was conducted, the results of which showed that the proposed storage model provides a improved performance compared with Jena.

Many researchers have developed frameworks that are capable of handling context information and can be adapted and used by any Web service. However, no research involving the systematic analysis of existing frameworks has yet been conducted. This paper examines the Context Framework, an example of existing frameworks, using a Petri net, and analyzes its advantages and disadvantages. Then, a Petri net model – with its disadvantages removed - is introduced, and a new framework is presented on the basis of that model. The proposed PAWS (Privacy Aware Web Services) framework has a expandability for context management and communicates flexible context information for every session. The proposed framework can solve overhead problems of context in SOAP messages. It also protects user privacy according to user preferences.

Ever since the network-based malicious code commonly known as a 'worm' surfaced in the early part of the 1980's, its prevalence has grown more and more. The RCS (Random Constant Spreading) worm has become a dominant, malicious virus in recent computer networking circles. The worm retards the availability of an overall network by exhausting resources such as CPU capacity, network peripherals and transfer bandwidth, causing damage to an uninfected system as well as an infected system. The generation and spreading cycle of these worms progress rapidly. The existing studies to counter malicious code have studied the Microscopic Model for detecting worm generation based on some specific pattern or sign of attack, thus preventing its spread by countering the worm directly on detection. However, due to zero-day threat actualization, rapid spreading of the RCS worm and reduction of survival time, securing a security model to ensure the survivability of the network became an urgent problem that the existing solution-oriented security measures did not address. This paper analyzes the recently studied efficient dynamic network. Essentially, this paper suggests a model that dynamically controls the RCS worm using the characteristics of Power-Law and depth distribution of the delivery node, which is commonly seen in preferential growth networks. Moreover, we suggest a model that dynamically controls the spread of the worm using information about the depth distribution of delivery. We also verified via simulation that the load for each node was minimized at an optimal depth to effectively restrain the spread of the worm.

Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

In this paper, we propose a framework for the real-time monitoring of wireless biosensors. This is a scalable platform that requires minimum human interaction during set-up and monitoring. Its main components include a biosensor, a smart gateway to automatically set up the body area network, a mechanism for delivering data to an Internet monitoring server, and automatic data collection, profiling and feature extraction from bio-potentials. Such a system could increase the quality of life and significantly lower healthcare costs for everyone in general, and for the elderly and those with disabilities in particular.

In this paper we propose a Differentiated Services Based Admission Control and Routing Algorithm for IPv6 (ACMRA). The basic DiffServ architecture lacks an admission control mechanism, the injection of more QoS sensitive traffic into the network can cause congestion at the core of the network. Our Differentiated Services Based Admission Control and Routing Algorithm for IPv6 combines the admission control phase with the route finding phase, and our routing protocol has been designed in a way to work alongside DiffServ based networks. The Differentiated Services Based Admission Control and Routing Algorithm for IPv6 constructs label switched paths in order to provide rigorous QoS provisioning. We have conducted extensive simulations to validate the effectiveness and efficiency of our proposed admission control and routing algorithm. Simulation Results show that the Differentiated Services Based Admission Control and Routing Algorithm for IPv6 provides an excellent packet delivery ratio, reduces the control packets¡¯ overhead, and makes use of the resources present on multiple paths to the destination network, while almost each admitted flow shows compliance with its Service Level Agreement.

To solve the general problems surrounding the application of genetic algorithms in stereo matching, two measures are proposed. Firstly, the strategy of simplified population-based incremental learning (PBIL) is adopted to reduce the problems with memory consumption search inefficiency£¬and a scheme for controlling the distance of neighbors for disparity smoothness is inserted to obtain a wide-area consistency of disparities. In addition, an alternative version of the proposed algorithm, without the use of a probability vector, is also presented for simpler set-ups. Secondly, programmable graphics-hardware (GPU) consists of multiple multi-processors and has a powerful parallelism which can perform operations in parallel at low cost. Therefore, in order to decrease the running time further, a model of the proposed algorithm, which can be run on programmable graphics-hardware (GPU), is presented for the first time. The algorithms are implemented on the CPU as well as on the GPU and are evaluated by experiments. The experimental results show that the proposed algorithm offers better performance than traditional BMA methods with a deliberate relaxation and its modified version in terms of both running speed and stability. The comparison of computation times for the algorithm both on the GPU and the CPU shows that the former has more speed-up than the latter, the bigger the image size is.

The awareness of boundaries in wireless sensor networks has many benefits. The identification of boundaries is especially challenging since typical wireless sensor networks consist of low-capability nodes that are unaware of their geographic location. In this paper, we propose a simple, efficient algorithm to detect nodes that are near the boundary of the sensor field as well as near the boundaries of holes. Our algorithm relies purely on the connectivity information of the underlying communication graph and does not require any information on the location of nodes. We introduce the 2-neighbor graph concept, and then make use of it to identify nodes near boundaries. The results of our experiment show that our algorithm carries out the task of topological boundary detection correctly and efficiently.

This paper proposes a utility-based data rate allocation algorithm to provide high-quality mobile video streaming over femtocell networks. We first derive a utility function to calculate the optimal data rates for maximizing the aggregate utilities of all mobile users in the femtocell. The total sum of optimal data rates is limited by the link capacity of the backhaul connections. Furthermore, electromagnetic cross-talk poses a serious problem for the backhaul connections, and its influence passes on to mobile users, as well as causing data rate degradation in the femtocell networks. We also have studied a fixed margin iterative water-filling algorithm to achieve the target data rate of each backhaul connection as a counter-measure to the cross-talk problem. The results of our simulation show that the algorithm is capable of minimizing the transmission power of backhaul connections while guaranteeing a high overall quality of service for all users of the same binder. In particular, it can provide the target data rate required to maximize user satisfaction with the mobile video streaming service over the femtocell networks.

As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) – if a pattern matching algorithm is used – can be reduced to O(mlogN) – if an Information Retrieval technique is adopted – while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval

Power consumed by modern computer systems, particularly servers in data centers has almost reached an unacceptable level. However, their energy consumption is often not justifiable when their utilization is considered; that is, they tend to consume more energy than needed for their computing related jobs. Task scheduling in distributed computing systems (DCSs) can play a crucial role in increasing utilization; this will lead to the reduction in energy consumption. In this paper, we address the problem of scheduling precedence-constrained parallel applications in DCSs, and present two energyconscious scheduling algorithms. Our scheduling algorithms adopt dynamic voltage and frequency scaling (DVFS) to minimize energy consumption. DVFS, as an efficient power management technology, has been increasingly integrated into many recent commodity processors. DVFS enables these processors to operate with different voltage supply levels at the expense of sacrificing clock frequencies. In the context of scheduling, this multiple voltage facility implies that there is a trade-off between the quality of schedules and energy consumption. Our algorithms effectively balance these two performance goals using a novel objective function and its variant, which take into account both goals; this claim is verified by the results obtained from our extensive comparative evaluation study.

Sensors are deployed to gather physical, environmental data in sensor networks. Depending on scenarios, it is often assumed that it is difficult for batteries to be recharged or exchanged in sensors. Thus, sensors should be able to process users¡¯ queries in an energy-efficient manner. This paper proposes a spatial query processing scheme- Minimum Bounding Area Based Scheme. This scheme has a purpose to decrease the number of outgoing messages during query processing. To do that, each sensor has to maintain some partial information locally about the locations of descendent nodes.
In the initial setup phase, the routing path is established. Each child node delivers to its parent node the location information including itself and all of its descendent nodes. A parent node has to maintain several minimum bounding boxes per child node. This scheme can reduce unnecessary message propagations for query processing. Finally, the experimental results show the effectiveness of the proposed scheme.

This research proposes a new neural network for text categorization which uses alternative representations of documents to numerical vectors. Since the proposed neural network is intended originally only for text categorization, it is called NTC (Neural Text Categorizer) in this research. Numerical vectors representing documents for tasks of text mining have inherently two main problems: huge dimensionality and sparse distribution. Although many various feature selection methods are developed to address the first problem, the reduced dimension remains still large. If the dimension is reduced excessively by a feature selection method, robustness of text categorization is degraded. Even if SVM (Support Vector Machine) is tolerable to huge dimensionality, it is not so to the second problem. The goal of this research is to address the two problems at same time by proposing a new representation of documents and a new neural network using the representation for its input vector.

Europeans are much more rigid in their thinking on robots and especially have a negative view on robots as peers since they regard robots as labor machines. Recently, Korea invented several educational robots as peer tutors. Therefore, study was needed to determine the difference in cultural acceptability for educational robots between Korea and Europe (Spain). We found that Europe seems to be much more rigid in its thinking on robots and especially has a negative view on educational robots. Korean parents have a strong tendency to see robots as 'the friend of children,' while on the other hand, European parents tend to see educational robots as 'machines or electronics'. Meanwhile, the expectation of children on educational robots showing identification content was higher in Europe than in Korea since European children are familiar with costume parties. This result implied that we may find a Korean market for educational robots earlier than a European market, but European children will be eager to play with educational robots even though their parents have a negative view of them.

VLSI chips have been tested using various automatic test equipment (ATE). Although each ATE has a similar structure, the language for ATE is proprietary and it is not easy to convert a test program for use among different ATE vendors. To address this difficulty we propose a tester structure expression language, a tester language with a novel format. The developed language is called the general tester language (GTL). Developing an interpreter for each tester, the GTL program can be directly applied to the ATE without conversion. It is also possible to select a cost-effective ATE from the test program, because the program expresses the required ATE resources, such as pin counts, measurement accuracy, and memory capacity. We describe the prototype environment for the GTL and the tester selection tool. The software size of the prototype is approximately 27,800 steps and 15 manmonths were required. Using the tester selection tool, the number of man-hours required in order to select an ATE could be reduced to 1/10. A GTL program was successfully executed on actual ATE.

The specification of the Home Evolved NodeB (Home-eNB), which is a small base station designed for use in residential or small business environment, is currently ongoing in 3GPP LTE (Long Term Evolution) systems. One of the key requirements for its feasibility in the LTE system is the mobility management in the deployment of the numerous Home-eNBs and other 3GPP network. In this paper, we overview the characteristic of Home-eNB and also describe the mobility management issues and the related approaches in 3GPP LTE based Home-eNB systems.

Human-Robot Interaction (HRI), based on already well-researched Human-Computer Interaction (HCI), has been under vigorous scrutiny since recent developments in robot technology. Robots may be more successful in establishing common ground in project-based education or foreign language learning for children than in traditional media. Backed by its strong IT environment and advances in robot technology, Korea has developed the world¡¯s first available e-Learning home robot. This has demonstrated the potential for robots to be used as a new educational media - robot-learning, referred to as ¡®r-Learning¡¯. Robot technology is expected to become more interactive and user-friendly than computers. Also, robots can exhibit various forms of communication such as gestures, motions and facial expressions. This study compared the effects of non-computer based (NCB) media (using a book with audiotape) and Web-Based Instruction (WBI), with the effects of Home Robot-Assisted Learning (HRL) for children. The robot gestured and spoke in English, and children could touch its monitor if it did not recognize their voice command. Compared to other learning programs, the HRL was superior in promoting and improving children¡¯s concentration, interest, and academic achievement. In addition, the children felt that a home robot was friendlier than other types of instructional media. The HRL group had longer concentration spans than the other groups, and the p-value demonstrated a significant difference in concentration among the groups. In regard to the children¡¯s interest in learning, the HRL group showed the highest level of interest, the NCB group and the WBI group came next in order. Also, academic achievement was the highest in the HRL group, followed by the WBI group and the NCB group respectively. However, a significant difference was also found in the children¡¯s academic achievement among the groups. These results suggest that home robots are more effective as regards children¡¯s learning concentration, learning interest and academic achievement than other types of instructional media (such as: books with audiotape and WBI) for English as a foreign language.

Although the Java bytecode has numerous advantages, it also has certain shortcomings such as its slow execution speed and difficulty of analysis. In order to overcome such disadvantages, a bytecode analysis and optimization must be performed. The control flow of the bytecode should be analyzed; next, information is required regarding where the variables are defined and used to conduct a dataflow analysis and optimization. There may be cases where variables with an identical name contain different values at different locations during execution, according to the value assigned to a given variable in each location. Therefore, in order to statically determine the value and type, the variables must be separated according to allocation. In order to achieve this, variables can be expressed using a static single assignment form. After transformation into a static single assignment form, the type information of each node expressed by each variable and expression must be configured to perform a static analysis and optimization. Based on the basic type information, this paper proposes a method for finding the related equivalent nodes, setting nodes with strong connection components, and efficiently assigning each node type

The use of agent paradigm in today¡¯s applications is hampered by the security concerns of agents and hosts alike. The agents require the presence of a secure and trusted execution environment; while hosts aim at preventing the execution of potentially malicious code. In general, hosts support the migration of agents through the provision of an agent server and managing the activities of arriving agents on the host. Numerous studies have been conducted to address the security concerns present in the mobile agent paradigm with a strong focus on the theoretical aspect of the problem. Various proposals in Intrusion Detection Systems aim at securing hosts in traditional client-server execution environments. The use of such proposals to address the security of agent hosts is not desirable since migrating agents typically execute on hosts as a separate thread of the agent server process. Agent servers are open to the execution of virtually any migrating agent; thus the intent or tasks of such agents cannot be known a priori. It is also conceivable that migrating agents may wish to hide their intentions from agent servers. In light of these observations, this work attempts to bridge the gap from theory to practice by analyzing the security mechanisms available in Aglet. We lay the foundation for implementation of application specific protocols dotted with access control, secured communication and ability to detect tampering of agent data. As agents exists in a distributed environment, our proposal also introduces a novel security framework to address the security concerns of hosts through collaboration and pattern matching even in the presence of differing views of the system. The introduced framework has been implemented on the Aglet platform and evaluated in terms of accuracy, false positive, and false negative rates along with its performance strain on the system.

The high-speed mobile Internet has recently been expanded, many attractive services are provided. However, these services require some form of security-related technology. This paper outlines Japanese mobile services and exposits some mobile security topics including mobile spam, mobile malware, mobile DRM system, mobile WiMAX security, and mobile key management.

One of the important functions of an Intelligent Transportation System (ITS) is to classify vehicle types using a vision system. We propose a method using machine-learning algorithms for this classification problem with 3-D object model fitting. It is also necessary to detect road lanes from a fixed traffic surveillance camera in preparation for model fitting. We apply a background mask and line analysis algorithm based on statistical measures to Hough Transform (HT) in order to remove noise and false positive road lanes. The results show that this method is quite efficient in terms of quality.

A PDA is used mainly for downloading data from a stationary server such as a desktop PC in an infrastructure network based on wireless LAN. Thus, the overall performance depends heavily on the performance of such downloading with PDA. Unfortunately, for a PDA the time taken to receive data from a PC is longer than the time taken to send it by 53%. Thus, we measured and analyzed all possible factors that could cause the receiving time of a PDA to be delayed with a test bed system. There are crucial factors: the TCP window size, file access time of a PDA, and the inter-packet delay that affects the receiving time of a PDA. The window size of a PDA during the downstream is reduced dramatically to 686 bytes from 32,581 bytes. In addition, because flash memory is embedded into a PDA, writing data into the flash memory takes twice as long as reading the data from it. To alleviate these, we propose three distinct remedies: First, in order to keep the window size at a sender constant, both the size of a socket send buffer for a desktop PC and the size of a socket receive buffer for a PDA should be increased. Second, to shorten its internal file access time, the size of an application buffer implemented in an application should be doubled. Finally, the inter-packet delay of a PDA and a desktop PC at the application layer should be adjusted asymmetrically to lower the traffic bottleneck between these heterogeneous terminals.

It is essential to guarantee a handoff dropping probability below a predetermined threshold for wireless mobile networks. Previous studies have proposed admission control policies for integrated voice/data traffic in wireless mobile networks. However, since QoS has been considered only in terms of CDP (Call Dropping Probability), the result has been a serious CBP (Call Blocking Probability) unfairness problem between voice and data traffic. In this paper, we suggest a new admission control policy that treats integrated voice and data traffic fairly while maintaining the CDP constraint. For underprivileged data traffic, which requires more bandwidth units than voice traffic, the packet is placed in a queue when there are no available resources in the base station, instead of being immediately rejected. Furthermore, we have adapted the biased coin method concept to adjust unfairness in terms of CBP. We analyzed the system model of a cell using both a two-dimensional continuous-time Markov chain and the Gauss-Seidel method. Numerical results demonstrate that our CAC (Call Admission Control) scheme successfully achieves CBP fairness for voice and data traffic.

With the explosive increase in the generation and utilization of spatiotemporal data sets, many research efforts have been focused on the efficient handling of the large volume of spatiotemporal sets. With the remarkable growth of ubiquitous computing technology, mining from the huge volume of spatiotemporal data sets is regarded as a core technology which can provide real world applications with intelligence. In this paper, we propose a 3-tier knowledge discovery framework for spatiotemporal data mining. This framework provides a foundation model not only to define the problem of spatiotemporal knowledge discovery but also to represent new knowledge and its relationships. Using the proposed knowledge discovery framework, we can easily formalize spatiotemporal data mining problems. The representation model is very useful in modeling the basic elements and the relationships between the objects in spatiotemporal data sets, information and knowledge.

A RMON agent system, which locates on a subnet, collects the network traffic information for management by retrieving and analyzing all of the packets on the subnet. The RMON agent system can miss some packets due to the high packet analyzing overhead when the number of packets on the subnet is huge. In this paper, we have developed a light-weight RMON agent system that can handle a large amount of packets without packet loss. Our RMON agent system has also been designed such that its functionality can be added dynamically when needed. To demonstrate the dynamic reconfiguration capability of our RMON agent system, a simple port scanning attack detection module is added to the RMON agent system. We have also evaluated the performance of our RMON agent system on a large network that has a huge traffic. The test result has shown our RMON agent system can analyze the network packets without packet loss.

In this study we propose an automatic reading system for diagnostic DNA chips. We define a general specification for an automatic reading system and propose a possible implementation method. The proposed system performs the whole reading process automatically without any user intervention, covering image acquisition, image analysis, and report generation. We applied the system for the automatic report generation of a commercialized DNA chip for cervical cancer detection. The fluorescence image of the hybridization result was acquired with a GenePixTM scanner using its library running in HTML pages. The processing of the acquired image and the report generation were executed by a component object module programmed with Microsoft Visual C++ 6.0. To generate the report document, we made an HWP 2002 document template with marker strings that were supposed to be searched and replaced with the corresponding information such as patient information and diagnosis results. The proposed system generates the report document by reading the template and changing the marker strings with the resultant contents. The system is expected to facilitate the usage of a diagnostic DNA chip for mass screening by the automation of a conventional manual reading process, shortening its processing time, and quantifying the reading criteria.

Vehicle detector system based on image processing technology is a significant domain of ITS (Intelligent Transportation System) applications due to its advantages such as low installation cost and it does not obstruct traffic during the installation of vehicle detection systems on the road[1]. In this paper, we propose architecture for vehicle detection by using image processing. The architecture consists of two main parts such as an image processing part, using high speed FPGA, decision and calculation part using CPU. The CPU part takes care of total system control and synthetic decision of vehicle detection. The FPGA part assumes charge of input and output image using video encoder and decoder, image classification and image memory control.

Image denoising is basic work for image processing, analysis and computer vision. This paper proposes a novel algorithm based on wavelet threshold for image denoising, which is combined with the linear CLS (Constrained Least Squares) filtering and thresholding methods in the transform domain. We demonstrated through simulations with images contaminated by white Gaussian noise that our scheme exhibits better performance in both PSNR (Peak Signal-to-Noise Ratio) and visual effect.

TRISO (Tri-Isotropic)-coated fuel particle is widely applied due to its higher stability at high temperature and its efficient retention capability for fission products in the HTGR (high temperature gas-cooled reactor), one of the highly efficient Generation IV reactors. The typical balltype TRISO-coated fuel particle with a diameter of about 1 mm is composed of a nuclear fuel particle as a kernel and of outer coating layers. The coating layers consist of a buffer PyC, inner PyC, SiC, and outer PyC layer. In this study, a digital image processing algorithm is proposed to automatically measure the thickness of the coating layers. An FBP (filtered backprojection) algorithm was applied to reconstruct the CT image using virtual X-ray radiographic images for a simulated TRISO-coated fuel particle. The automatic measurement algorithm was developed to measure the coating thickness for the reconstructed image with noises. The boundary lines were automatically detected, then the coating thickness was circularly by the algorithm. The simulation result showed that the measurement error rate was less than 1.4%.

Recently, concomitant with a surge in numbers of Internet of Things (IoT) devices with various sensors, mobile
crowdsensing (MCS) has provided a new business model for IoT. For example, a person can share road traffic
pictures taken with their smartphone via a cloud computing system and the MCS data can provide benefits to
other consumers. In this service model, to encourage people to actively engage in sensing activities and to
voluntarily share their sensing data, providing appropriate incentives is very important. However, the sensing
data from personal devices can be sensitive to privacy, and thus the privacy issue can suppress data sharing.
Therefore, the development of an appropriate privacy protection system is essential for successful MCS. In this
study, we address this problem due to the conflicting objectives of privacy preservation and incentive payment.
We propose a privacy-preserving mechanism that protects identity and location privacy of sensing users
through an on-demand incentive payment and group signatures methods. Subsequently, we apply the proposed
mechanism to one example of MCS—an intelligent parking system—and demonstrate the feasibility and
efficiency of our mechanism through emulation.

Crowdsensing technologies can improve the efficiency of smart parking system in comparison with present
sensor based smart parking system because of low install price and no restriction caused by sensor installation.
A lot of sensing data is necessary to predict parking lot saturation in real-time. However in real world, it is hard
to reach the required number of sensing data. In this paper, we model a saturation predication combining a
time-based prediction model and a sensing data-based prediction model. The time-based model predicts
saturation in aspects of parking lot location and time. The sensing data-based model predicts the degree of
saturation of the parking lot with high accuracy based on the degree of saturation predicted from the first model,
the saturation information in the sensing data, and the number of parking spaces in the sensing data. We
perform prediction model learning with real sensing data gathered from a specific parking lot. We also evaluate
the performance of the predictive model and show its efficiency and feasibility.

Indexing

JIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).

This journal was supported by the Korean Federation of Science and Technology Societies Grant funded by the Korean Government (Ministry of Education).

Society

ABOUT THE SOCIETY

Ever since information processing became one of the most important industries in the country, computing professionals have encountered a growing number of challenges.
Along with scholars and colleagues in related fields, they have gathered together at a variety of forums and meetings over the last few decades to share their knowledge and experiences,
and the outcomes of their research. These exchanges led to the founding of the Korea Information Processing Society (KIPS) on January 15, 1993. The KIPS was registered as an incorporated association under the Ministry of Science,
ICT and Future Planning under the government of the Republic of Korea. The main purpose of the KIPS organization is to improve our society by achieving the highest capability possible in the domain of information technology.
As such, it focuses on close collaboration with the nationâs industry, academic, and research communities to foster technological innovation,
to enhance its members' careers, and to promote the advanced information processing industry.